The effect of a dominant shrub on annuals plants in a desert system changes along a gradient of precipitation.
Alex Filazzola
May 2017
Ephedra regional gradient (ERG) analyses
Abstract
An under-examined component of the shrub-annual relationship is how regional drivers, such as climate, may alter the sign or magnitude of positive interactions. Interspecific interactions between plants have been shown to be strongly linked to climate, particularly temperature and precipitation. The stress-gradient hypothesis (SGH) predicts that higher abiotic stress (i.e. for deserts, increasing temperature and reduced precipitation) will increase the frequency of positive interactions among shrubs and their annual understory. Regional climate gradients also have indirect effects on plant composition, such as determining consumer abundance and soil nutrient composition. Nutrient availability is particularly affected by precipitation because of altered decomposition rates of organic matter and mineralization. Therefore, the strength of facilitation and operating mechanism of a shrub on the annual plant community may change along a regional gradient.
Hypothesis
We tested the hypothesis that positive interactions among shrubs and annual plants will increase with abiotic stress and reduce nutrient availability along a regional gradient of aridity.
Methods
Weather for growing season
Climate patterns within study
season1.sjd <- season1 %>% filter(Gradient<4) %>% group_by(year, month,days) %>% summarise_if(is.numeric, funs(mean(., na.rm=T)))
season1.mnp <- season1 %>% filter(Gradient>3)%>% group_by(year, month,days) %>% summarise_if(is.numeric, funs(mean(., na.rm=T)))
season2.sjd <- season2 %>% filter(Gradient<4) %>% group_by(year, month,days) %>% summarise_if(is.numeric, funs(mean(., na.rm=T)))
season2.mnp <- season2 %>% filter(Gradient>3)%>% group_by(year, month,days) %>% summarise_if(is.numeric, funs(mean(., na.rm=T)))
## Rain vs Temperature in 2016
par(mfrow=c(2,1))
par(mar=c(1.5,4.5,1,4.5))
plot1 <- barplot(height=season1.sjd$Precip, ylim=c(0,14), ylab="Average precipitation at all sites (cm)")
points(plot1[,1], season1.sjd$min.temp, type="l", col="#FF000050", lwd=2)
axis(4, at=seq(0,14,2), lab=seq(0,14,2), ylab="")
mtext("Average temperature at all sites (°C)", 4, line=3)
par(mar=c(4.5,4.5,0,4.5))
plot1 <- barplot(height=season1.mnp$Precip, ylim=c(0,14), ylab="Average precipitation at all sites (cm)")
axis(1, plot1[c(1,30,60,90,120,150,180)], c("Nov","Dec","Jan","Feb","Mar","Apr","May"))
points(plot1[,1], season1.mnp$min.temp, type="l", col="#FF000050", lwd=2)
axis(4, at=seq(0,14,2), lab=seq(0,14,2), ylab="")
mtext("Average temperature at all sites (°C)", 4, line=3)
## Rain vs Temperature in 2017
par(mfrow=c(2,1))
par(mar=c(1.5,4.5,1,4.5))
plot2 <- barplot(height=season2.sjd$Precip, ylim=c(0,14), ylab="Average precipitation at all sites (cm)")
points(plot2[,1], season2.sjd$min.temp, type="l", col="#FF000050", lwd=2)
axis(4, at=seq(0,14,2), lab=seq(0,14,2), ylab="")
mtext("Average temperature at all sites (°C)", 4, line=3)
par(mar=c(4.5,4.5,0,4.5))
plot2 <- barplot(height=season2.mnp$Precip, ylim=c(0,14), ylab="Average precipitation at all sites (cm)")
points(plot2[,1], season2.mnp$min.temp, type="l", col="#FF000050", lwd=2)
axis(1, plot1[c(1,30,60,90,120,150,180)], c("Nov","Dec","Jan","Feb","Mar","Apr","May"))
axis(4, at=seq(0,14,2), lab=seq(0,14,2), ylab="")
mtext("Average temperature at all sites (°C)", 4, line=3)
### 2016 The rain was inconsistent and mostly absent in the Mojave. This resulted in low germination and producitivty at the southern sites
### 2017 The rain was more plentiful, but in the northern sites, there appears to be a frost period after the majority of the rainfall. Need to check number of frost days
season1.frost <- season1 %>% group_by(Site) %>% summarize(frost.days=sum(min.temp<0, na.rm=T)/length(min.temp)*100)
data.frame(season1.frost)## Site frost.days
## 1 Barstow 24.725275
## 2 Cuyama 29.670330
## 3 HeartofMojave 25.824176
## 4 PanocheHills 10.439560
## 5 SheepholeValley 6.043956
## 6 Tecopa 23.626374
## 7 TejonRanch 50.549451season2.frost <- season2 %>% group_by(Site) %>% summarize(frost.days=sum(min.temp<0, na.rm=T)/length(min.temp)*100)
data.frame(season2.frost)## Site frost.days
## 1 Barstow 17.679558
## 2 Cuyama 19.889503
## 3 HeartofMojave 16.574586
## 4 PanocheHills 14.917127
## 5 SheepholeValley 1.785714
## 6 Tecopa 25.966851
## 7 TejonRanch 35.911602## Both years had comparable number of frost days
## Compare number of consecutive frost days (i.e. frost periods)
season1[,"frost"] <- ifelse(season1$min.temp<0, -99,season1$min.temp) ## identified days below freezing
season2[,"frost"] <- ifelse(season2$min.temp<0, -99,season2$min.temp) ## identified days below freezing
count.consec <- function(x) {max(rle(as.character(x))$lengths)}
season1.frost <- season1 %>% group_by(Site) %>% summarize(count.consec(frost))
data.frame(season1.frost)## Site count.consec.frost.
## 1 Barstow 10
## 2 Cuyama 10
## 3 HeartofMojave 12
## 4 PanocheHills 5
## 5 SheepholeValley 7
## 6 Tecopa 11
## 7 TejonRanch 14season2.frost <- season2 %>% group_by(Site) %>% summarize(count.consec(frost))
data.frame(season2.frost)## Site count.consec.frost.
## 1 Barstow 5
## 2 Cuyama 6
## 3 HeartofMojave 7
## 4 PanocheHills 6
## 5 SheepholeValley 3
## 6 Tecopa 7
## 7 TejonRanch 13## compare only after plants have germinated
season1.frost <- season1 %>% group_by(Site) %>% filter(year>2015) %>% summarize(frost.days=sum(min.temp<0, na.rm=T)/length(min.temp)*100, avg.min.temp=mean(min.temp, na.rm=T))
data.frame(season1.frost)## Site frost.days avg.min.temp
## 1 Barstow 12.396694 5.750413
## 2 Cuyama 11.570248 3.352893
## 3 HeartofMojave 16.528926 4.542149
## 4 PanocheHills 2.479339 6.777686
## 5 SheepholeValley 2.479339 9.394167
## 6 Tecopa 11.570248 6.342149
## 7 TejonRanch 33.884298 1.438017season2.frost <- season2 %>% group_by(Site) %>% filter(year>2016) %>% summarize(frost.days=sum(min.temp<0, na.rm=T)/length(min.temp)*100,avg.min.temp=mean(min.temp, na.rm=T))
data.frame(season2.frost)## Site frost.days avg.min.temp
## 1 Barstow 11.666667 6.052500
## 2 Cuyama 16.666667 3.624167
## 3 HeartofMojave 8.333333 5.637838
## 4 PanocheHills 8.333333 6.065833
## 5 SheepholeValley 0.000000 9.386916
## 6 Tecopa 20.833333 5.938333
## 7 TejonRanch 28.333333 2.535833season1.frost <- season1 %>% group_by(Site) %>% filter(year>2015) %>% summarize(count.consec(frost))
data.frame(season1.frost)## Site count.consec.frost.
## 1 Barstow 5
## 2 Cuyama 6
## 3 HeartofMojave 4
## 4 PanocheHills 2
## 5 SheepholeValley 2
## 6 Tecopa 4
## 7 TejonRanch 10season2.frost <- season2 %>% group_by(Site) %>% filter(year>2016)%>% summarize(count.consec(frost))
data.frame(season2.frost)## Site count.consec.frost.
## 1 Barstow 5
## 2 Cuyama 6
## 3 HeartofMojave 3
## 4 PanocheHills 3
## 5 SheepholeValley 2
## 6 Tecopa 5
## 7 TejonRanch 10Microenvironmental differences
We compared temperature and relative humidity between shrub and open microsites among all sites along the regional gradient 
Shapiro-Wilk normality test
data: fit1$residuals
W = 0.99375, p-value = 0.07069 Df Sum Sq Mean Sq F value Pr(>F)
micro 1 1.804 1.8040 159.02 < 2e-16 ***
gradient 6 17.302 2.8837 254.20 < 2e-16 ***
micro:gradient 6 0.775 0.1292 11.39 8.29e-12 ***
Residuals 420 4.765 0.0113
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Analysis of Deviance Table
Model: binomial, link: logit
Response: RH/100
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev Pr(>Chi)
NULL 433 60.430
micro 1 0.005 432 60.426 0.9465
gradient 6 53.103 426 7.323 1.119e-09 ***
micro:gradient 6 1.848 420 5.475 0.9331
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Analysis of Deviance Table
Model: Gamma, link: inverse
Response: swc
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev F Pr(>F)
NULL 404 231.405
Site 6 189.683 398 41.722 381.2179 <2e-16 ***
Microsite 1 0.287 397 41.435 3.4579 0.0637 .
Site:Microsite 6 0.696 391 40.739 1.3994 0.2136
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Analysis of Deviance Table
Model: Gamma, link: inverse
Response: swc
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev F Pr(>F)
NULL 419 183.193
Site 6 144.930 413 38.263 279.4537 <2e-16 ***
Microsite 1 0.080 412 38.183 0.9284 0.3358
Site:Microsite 6 0.535 406 37.647 1.0322 0.4037
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
site level means
## site coordintes
site.gps <- read.csv("Data/ERGsites.csv")
# nutrient data for each site
nutrients <- read.csv("Data/ERG.soilnutrients.csv")
## long term climate data extracted from worldclim
clim.dat <- read.csv("Data/ERG.worldclim.csv")
## check correlation among long-term climate variables
cor(clim.dat[,3:8]) ## Temp.wettest.QR least correlated ## Annual.Temp Temp.Seasonality Temp.wettest.QR
## Annual.Temp 1.0000000 0.9051791 0.7260617
## Temp.Seasonality 0.9051791 1.0000000 0.4650548
## Temp.wettest.QR 0.7260617 0.4650548 1.0000000
## Annual.precip -0.8911069 -0.9650533 -0.5313460
## Precip.seasonality -0.9715419 -0.9774646 -0.6165170
## Precipt.wettest.QR -0.8839124 -0.9570957 -0.5523347
## Annual.precip Precip.seasonality Precipt.wettest.QR
## Annual.Temp -0.8911069 -0.9715419 -0.8839124
## Temp.Seasonality -0.9650533 -0.9774646 -0.9570957
## Temp.wettest.QR -0.5313460 -0.6165170 -0.5523347
## Annual.precip 1.0000000 0.9613729 0.9980959
## Precip.seasonality 0.9613729 1.0000000 0.9543657
## Precipt.wettest.QR 0.9980959 0.9543657 1.0000000## Obtain mean shrub traits for each site
shrubs <- read.csv("Data/ERG.shrub.csv")
shrubs <- subset(shrubs, Microsite=="shrub")
shrubs.mean <- shrubs %>% group_by(Gradient,Site) %>% summarise_all(funs(mean))
shrubs.mean <- data.frame(shrubs.mean)
shrubs.vars <- shrubs.mean[,c("Site","Gradient","volume","canopy","Dx","DxEph","Compaction")]
## test correlation among shrub traits
cor(shrubs.vars[,3:7]) ## compaction x volume & DxEph x Dx high correlation## volume canopy Dx DxEph Compaction
## volume 1.0000000 0.2243902 0.40036651 0.2631563 -0.64927168
## canopy 0.2243902 1.0000000 -0.68479570 -0.3538094 -0.20833287
## Dx 0.4003665 -0.6847957 1.00000000 0.7803527 -0.06531485
## DxEph 0.2631563 -0.3538094 0.78035272 1.0000000 0.33769574
## Compaction -0.6492717 -0.2083329 -0.06531485 0.3376957 1.00000000vifstep(shrubs.vars[,3:7], th=10) ## Dx showing collinearity problems## 1 variables from the 5 input variables have collinearity problem:
##
## Dx
##
## After excluding the collinear variables, the linear correlation coefficients ranges between:
## min correlation ( Compaction ~ canopy ): -0.2083329
## max correlation ( Compaction ~ volume ): -0.6492717
##
## ---------- VIFs of the remained variables --------
## Variables VIF
## 1 volume 4.043473
## 2 canopy 1.474526
## 3 DxEph 2.874621
## 4 Compaction 3.760284shrub.site <- shrubs.vars[,-c(1,2,5)]
rownames(shrub.site) <- shrubs.vars[,1]
## PCA of shrub characteristics
pca1 <- prcomp(log(shrub.site), scale=T)
plot(pca1)
biplot(pca1, scale = T)
summary(pca1) ## 77% variation explained## Importance of components:
## PC1 PC2 PC3 PC4
## Standard deviation 1.2958 1.1879 0.8922 0.33722
## Proportion of Variance 0.4198 0.3528 0.1990 0.02843
## Cumulative Proportion 0.4198 0.7725 0.9716 1.00000## PCA of site characteristics for season1
## Obtain mean weather variables for each site
season1.mean <- season1 %>% group_by(Gradient,Site) %>% summarise(temp.var=var(avg.temp,na.rm=T),Precip=sum(Precip),wind=mean(wind.speed, na.rm=T))
season1.mean <- data.frame(season1.mean)
## extract key variables
## dropped RH, and chose min.temp because least correlation with others & cold stress
##combine nutrients and long-term averages
site.vars <- data.frame(season1.mean[,3:5],site.gps["elevation"]) ## drop Phosphorus and other climate variables because of correlations,
row.names(site.vars) <- shrubs.vars[,1]
cor(site.vars)## temp.var Precip wind elevation
## temp.var 1.00000000 -0.8679054 0.05816084 -0.3100598
## Precip -0.86790537 1.0000000 -0.37279135 0.1197715
## wind 0.05816084 -0.3727914 1.00000000 0.4680108
## elevation -0.31005981 0.1197715 0.46801084 1.0000000site.vars2016 <- site.vars
## check for collinearity
vifstep(site.vars, th=10) ## remove potassium and temperature minimum## No variable from the 4 input variables has collinearity problem.
##
## The linear correlation coefficients ranges between:
## min correlation ( wind ~ temp.var ): 0.05816084
## max correlation ( Precip ~ temp.var ): -0.8679054
##
## ---------- VIFs of the remained variables --------
## Variables VIF
## 1 temp.var 6.190318
## 2 Precip 7.022018
## 3 wind 2.120866
## 4 elevation 1.500626pca1 <- prcomp(log(abs(site.vars)), scale=T)
plot(pca1)
biplot(pca1)
## check contribution of loadings
pca1$rotation## PC1 PC2 PC3 PC4
## temp.var 0.6340914 -0.05810223 0.57710857 -0.5113687
## Precip -0.6784404 -0.15132401 0.09928838 -0.7120123
## wind 0.1851081 0.75272565 -0.48565259 -0.4040800
## elevation -0.3215303 0.63806683 0.64902163 0.2612660aload <- abs(pca1$rotation)
sweep(aload, 2, colSums(aload), "/")## PC1 PC2 PC3 PC4
## temp.var 0.3485608 0.03630893 0.31865593 0.2707478
## Precip 0.3729395 0.09456458 0.05482301 0.3769800
## wind 0.1017541 0.47038923 0.26815765 0.2139430
## elevation 0.1767456 0.39873726 0.35836340 0.1383291summary(pca1) ## 89% variation explained## Importance of components:
## PC1 PC2 PC3 PC4
## Standard deviation 1.4405 1.1996 0.6867 0.12018
## Proportion of Variance 0.5188 0.3597 0.1179 0.00361
## Cumulative Proportion 0.5188 0.8785 0.9964 1.00000gradient1.season1 <- pca1$x[,1]
gradient2.season1 <- pca1$x[,2]
## season2
season2.mean <- season2 %>% group_by(Gradient,Site) %>% summarise(temp.var=var(avg.temp,na.rm=T),Precip=sum(Precip, na.rm=T),wind=mean(wind.speed, na.rm=T))
season2.mean <- data.frame(season2.mean)
## extract key variables
## dropped RH, and chose min.temp because least correlation with others & cold stress
##combine nutrients and long-term averages
site.vars <- data.frame(season2.mean[,3:5],site.gps["elevation"]) ## drop Phosphorus and other climate variables because of correlations,
row.names(site.vars) <- shrubs.vars[,1]
cor(site.vars)## temp.var Precip wind elevation
## temp.var 1.0000000 -0.7427028 -0.2262409 -0.3996999
## Precip -0.7427028 1.0000000 -0.1551517 0.2554805
## wind -0.2262409 -0.1551517 1.0000000 0.5792844
## elevation -0.3996999 0.2554805 0.5792844 1.0000000site.vars2017 <- site.vars
## check for collinearity
vifstep(site.vars, th=10) ## remove potassium and temperature minimum## No variable from the 4 input variables has collinearity problem.
##
## The linear correlation coefficients ranges between:
## min correlation ( wind ~ Precip ): -0.1551517
## max correlation ( Precip ~ temp.var ): -0.7427028
##
## ---------- VIFs of the remained variables --------
## Variables VIF
## 1 temp.var 3.041063
## 2 Precip 3.208232
## 3 wind 2.155356
## 4 elevation 1.844735pca2 <- prcomp(log(abs(site.vars)), scale=T)
plot(pca2)
biplot(pca2)
summary(pca2) ## 85% variation explained## Importance of components:
## PC1 PC2 PC3 PC4
## Standard deviation 1.5002 1.1069 0.62814 0.36016
## Proportion of Variance 0.5626 0.3063 0.09864 0.03243
## Cumulative Proportion 0.5626 0.8689 0.96757 1.00000## check contribution of loadings
pca2$rotation## PC1 PC2 PC3 PC4
## temp.var 0.5836474 -0.2879037 0.46403105 0.6009512
## Precip -0.5465748 0.4632885 0.07702085 0.6933164
## wind -0.3004017 -0.7480738 -0.49872544 0.3184610
## elevation -0.5199717 -0.3779695 0.72802192 -0.2382280aload <- abs(pca2$rotation)
sweep(aload, 2, colSums(aload), "/")## PC1 PC2 PC3 PC4
## temp.var 0.2992150 0.1533658 0.26249081 0.3246706
## Precip 0.2802092 0.2467930 0.04356877 0.3745719
## wind 0.1540051 0.3984976 0.28211656 0.1720521
## elevation 0.2665708 0.2013437 0.41182386 0.1287053## define gradients
gradient1.season2 <- pca2$x[,1]
gradient2.season2 <- pca2$x[,2]Compare gradient to phytometer
mean.phyto <- census %>% filter(Census=="end") %>%group_by(Year, Gradient, Site, Microsite) %>% summarise_if(is.numeric, funs(mean(., na.rm=T)))
mean.phyto <- data.frame(mean.phyto)
season1.mean <- season1 %>% group_by(Gradient,Site) %>% summarise(temp.var=var(avg.temp,na.rm=T),Precip=sum(Precip),wind=mean(wind.speed, na.rm=T), max.temp=abs(mean(max.temp, na.rm=T)))
s1.mean <- data.frame(season1.mean)
season2.mean <- season2 %>% group_by(Gradient,Site) %>% summarise(temp.var=var(avg.temp,na.rm=T),Precip=sum(Precip, na.rm=T),wind=mean(wind.speed, na.rm=T), max.temp=abs(mean(max.temp, na.rm=T)))
s2.mean <- data.frame(season2.mean)
gradient1.season1 <- (s1.mean[,"Precip"]-s1.mean[,"max.temp"])/10
gradient1.season2 <- (s2.mean[,"Precip"]-s2.mean[,"max.temp"])/10
phyto.2016 <- subset(mean.phyto, Year==2016)
plot(gradient1.season1+1, phyto.2016[phyto.2016$Microsite=="open","phyto.biomass"], ylim=c(0,2))
points(gradient1.season1-1, phyto.2016[phyto.2016$Microsite=="shrub","phyto.biomass"], pch=19)
phyto.2017 <- subset(mean.phyto, Year==2017)
plot(gradient1.season2, phyto.2017[phyto.2017$Microsite=="open","phyto.biomass"], ylim=c(0,3))
points(gradient1.season2, phyto.2017[phyto.2017$Microsite=="shrub","phyto.biomass"], pch=19)
Differences from Nutrient content
## Nitrogen difference between shrub and sites
m.nit <- aov(log(N) ~ site * microsite, data=nutrients)
summary(m.nit)## Df Sum Sq Mean Sq F value Pr(>F)
## site 6 63.04 10.51 13.241 2.86e-09 ***
## microsite 1 40.16 40.16 50.614 2.24e-09 ***
## site:microsite 6 4.96 0.83 1.042 0.408
## Residuals 56 44.43 0.79
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(m.nit, "microsite")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log(N) ~ site * microsite, data = nutrients)
##
## $microsite
## diff lwr upr p adj
## shrub-open 1.514873 1.088317 1.941429 0## Potassium difference between shrub and sites
m.pot <- aov(log(K) ~ site * microsite, data=nutrients)
summary(m.pot)## Df Sum Sq Mean Sq F value Pr(>F)
## site 6 15.234 2.539 20.46 1.62e-12 ***
## microsite 1 4.019 4.019 32.39 4.80e-07 ***
## site:microsite 6 1.571 0.262 2.11 0.0664 .
## Residuals 56 6.948 0.124
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(m.pot, "microsite")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log(K) ~ site * microsite, data = nutrients)
##
## $microsite
## diff lwr upr p adj
## shrub-open 0.4792314 0.310559 0.6479038 5e-07## Phosphorus difference between shrub and sites
m.pho <- aov(log(P) ~ site * microsite, data=nutrients)
summary(m.pho)## Df Sum Sq Mean Sq F value Pr(>F)
## site 6 23.163 3.861 14.33 8.10e-10 ***
## microsite 1 10.546 10.546 39.15 5.79e-08 ***
## site:microsite 6 3.394 0.566 2.10 0.0676 .
## Residuals 56 15.085 0.269
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(m.pho, "microsite")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log(P) ~ site * microsite, data = nutrients)
##
## $microsite
## diff lwr upr p adj
## shrub-open 0.7762734 0.5277317 1.024815 1e-07annual plant community repsonse
spp.data <- read.csv("Data/ERG.communitydata.csv")
spp.data[is.na(spp.data)] <- 0
mean.spp <- spp.data %>% group_by(Year,Site) %>% summarize(abd=mean(Abundance), richness=mean(Richness), biomass=mean(Biomass))
mean.spp <- data.frame(mean.spp)
## sort to gradient from alphabetical
alpha.grad <- c(4,2,5,1,6,7,3)
mean.spp2016 <- subset(mean.spp, Year==2016)
mean.spp2016 <-mean.spp[order(alpha.grad),]
## community responses
plot(gradient1.season1, mean.spp2016[,"richness"]) ## explains trends with one outlier
m1 <- lm(richness~gradient1.season1, data=mean.spp2016)
summary(m1)##
## Call:
## lm(formula = richness ~ gradient1.season1, data = mean.spp2016)
##
## Residuals:
## 4 2 7 1 3 5 6
## -0.7006 0.7022 1.3742 -1.7940 1.3879 -1.2670 0.2974
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.81086 0.68451 2.645 0.0457 *
## gradient1.season1 0.04821 0.07411 0.651 0.5441
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 5 degrees of freedom
## Multiple R-squared: 0.07803, Adjusted R-squared: -0.1064
## F-statistic: 0.4232 on 1 and 5 DF, p-value: 0.5441plot(gradient1.season1, mean.spp2016[,"abd"]) ## explains trends with one outlier
m2 <- lm(abd~gradient1.season1, data=mean.spp2016) ## trend
summary(m2)##
## Call:
## lm(formula = abd ~ gradient1.season1, data = mean.spp2016)
##
## Residuals:
## 4 2 7 1 3 5 6
## 19.671 -37.660 3.599 2.445 11.246 -8.390 9.089
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2211 10.0748 -0.022 0.98334
## gradient1.season1 6.3754 1.0908 5.845 0.00208 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.51 on 5 degrees of freedom
## Multiple R-squared: 0.8723, Adjusted R-squared: 0.8468
## F-statistic: 34.16 on 1 and 5 DF, p-value: 0.002075plot(gradient2.season1, mean.spp2016[,"biomass"]) ## explains trends with one outlier
m3 <- lm(biomass~gradient1.season1, data=mean.spp2016) ## trend
summary(m3)##
## Call:
## lm(formula = biomass ~ gradient1.season1, data = mean.spp2016)
##
## Residuals:
## 4 2 7 1 3 5 6
## 2.3417 -3.2663 -1.4100 0.5527 2.2333 -0.1950 -0.2565
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.3699 1.0657 -0.347 0.74266
## gradient1.season1 0.5242 0.1154 4.543 0.00615 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.17 on 5 degrees of freedom
## Multiple R-squared: 0.805, Adjusted R-squared: 0.766
## F-statistic: 20.64 on 1 and 5 DF, p-value: 0.006151##season2
## sort to gradient from alphabetical
alpha.grad <- c(4,2,5,1,6,7,3)
mean.spp <- subset(mean.spp, Year==2017)
mean.spp <-mean.spp[order(alpha.grad),]
## community responses season2
plot(gradient2.season2, mean.spp[,"richness"]) ## explains trends with one outlier
m1 <- lm(richness~gradient1.season2, data=mean.spp)
summary(m1)##
## Call:
## lm(formula = richness ~ gradient1.season2, data = mean.spp)
##
## Residuals:
## 11 9 14 8 10 12 13
## -0.37132 0.12774 0.97167 -0.26409 0.13589 -0.07311 -0.52677
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.439002 0.417135 5.847 0.00207 **
## gradient1.season2 -0.006897 0.032506 -0.212 0.84034
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5421 on 5 degrees of freedom
## Multiple R-squared: 0.008924, Adjusted R-squared: -0.1893
## F-statistic: 0.04502 on 1 and 5 DF, p-value: 0.8403plot(gradient1.season2, mean.spp[,"abd"]) ## explains trends with one outlier
m2 <- lm(abd~gradient1.season2, data=mean.spp) ## trend
summary(m2)##
## Call:
## lm(formula = abd ~ gradient1.season2, data = mean.spp)
##
## Residuals:
## 11 9 14 8 10 12 13
## 30.369 -30.430 -6.459 -13.195 1.749 11.492 6.475
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -47.123 16.291 -2.893 0.034083 *
## gradient1.season2 9.138 1.270 7.198 0.000806 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 21.17 on 5 degrees of freedom
## Multiple R-squared: 0.912, Adjusted R-squared: 0.8944
## F-statistic: 51.82 on 1 and 5 DF, p-value: 0.0008059plot(gradient1.season2, mean.spp[,"biomass"]) ## explains trends with one outlier
m3 <- lm(biomass~gradient1.season2, data=mean.spp) ## trend
summary(m3)##
## Call:
## lm(formula = biomass ~ gradient1.season2, data = mean.spp)
##
## Residuals:
## 11 9 14 8 10 12 13
## 2.84669 -2.69808 -1.22403 -0.42398 0.18218 1.39556 -0.07834
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.3322 1.5019 -1.553 0.181
## gradient1.season2 0.4507 0.1170 3.851 0.012 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.952 on 5 degrees of freedom
## Multiple R-squared: 0.7479, Adjusted R-squared: 0.6975
## F-statistic: 14.83 on 1 and 5 DF, p-value: 0.01199RDA community and environmental data
community <- read.csv("Data/ERG.communitydata.csv")
community[is.na(community)] <- 0
##2016
## add enviro data to dataframe
comm2016 <- subset(community, Year==2016)
site.vars2016[,"Site"] <- as.factor(row.names(site.vars2016))
comm2016 <- merge(comm2016, site.vars2016, by="Site")
## transform spp data
community.trans <- decostand(comm2016[,13:53], "hellinger")
rda1 <- rda(community.trans, comm2016[,54:57])
(R2adj <- RsquareAdj(rda1)$adj.r.squared) ## 50.4% of variation explained## [1] 0.4249994## build byplot manually
par(mar=c(4.5,4.5,0.5,0.5))
plot(rda1, type="n", xlim=c(-1,1), ylim=c(-1.5,1.5))
## calculate priority
spp.priority <- colSums(comm2016[,13:53])
## plot RDA1
colvec <- rep(c("blue","dodgerblue3","cyan","yellow2","orange","tomato","red2"),each=60)
points(rda1, display = "sites", col = "black", pch = c(21), bg = colvec)
orditorp(rda1, display = "species", cex = 0.7, col = "darkred", priority=spp.priority, air=0.5)
text(rda1, display = "bp", col = "blue", cex = 0.8)
legend("bottomright", pch=c(21), legend=unique(comm2016$Site), pt.bg=unique(colvec), cex=1)
## ordikplot to adjust species locations
## 2017
## add enviro data to dataframe
comm2017 <- subset(community, Year==2017)
site.vars[,"Site"] <- as.factor(row.names(site.vars))
comm2017 <- merge(comm2017, site.vars, by="Site")
#
# ## transform spp data
# community.trans <- decostand(comm2017[,13:53], "hellinger")
#
# rda2 <- rda(community.trans, comm2017[,54:58])
# (R2adj <- RsquareAdj(rda2)$adj.r.squared) ## 50.4% of variation explained
#
#
# ## build byplot manually
# par(mar=c(4.5,4.5,0.5,0.5))
# plot(rda2, type="n", xlim=c(-1,1), ylim=c(-1.5,1.5))
#
# ## calculate priority
# spp.priority <- colSums(comm2017[,13:53])
#
# ## plot RDA2
# colvec <- rep(c("blue","dodgerblue3","cyan","yellow2","orange","tomato","red2"),each=60)
# points(rda2, display = "sites", col = "black", pch = c(21), bg = colvec)
# orditorp(rda2, display = "species", cex = 0.7, col = "darkred", priority=spp.priority, air=0.5)
# text(rda2, display = "bp", col = "blue", cex = 0.8)
# legend("bottomright", pch=c(21), legend=unique(comm2016$Site), pt.bg=unique(colvec), cex=1)
# ## ordikplot to adjust species locationsPhytometer analysis
census <- read.csv("Data/ERG.phytometer.census.csv")
census[is.na(census)] <- 0
census[,"phyto.abd"] <- rowSums(census[,c("Phacelia","Plantago","Salvia")])
## calculate the number of seeds per gram
seed.mass <- read.csv("Data/seed.mass.csv")
seed.mass.avg <- seed.mass %>% group_by(species) %>% summarize(avg.seed.gram=mean(seed.number),se.seed.gram=se(seed.number))
seed.mass.avg <- data.frame(seed.mass.avg)
seed.mass.avg## species avg.seed.gram se.seed.gram
## 1 Amsinkia 235.2 7.611833
## 2 Caulanthus 3019.0 8.916277
## 3 Lipidium 752.4 4.261455
## 4 Monolopia 737.8 7.831986
## 5 Phacelia 772.0 5.300943
## 6 Plantago 558.2 6.865858
## 7 Salvia 980.0 9.612492## get proportion of seed germinated per 0.3 grams of seed
census[,"Phacelia.prop"] <- census[,"Phacelia"]/(seed.mass.avg$avg.seed.gram[which(seed.mass.avg$species=="Phacelia")]*0.3)
census[,"Plantago.prop"] <- census[,"Plantago"]/(seed.mass.avg$avg.seed.gram[which(seed.mass.avg$species=="Plantago")]*0.3)
census[,"Salvia.prop"] <- census[,"Salvia"]/(seed.mass.avg$avg.seed.gram[which(seed.mass.avg$species=="Salvia")]*0.3)
## beginning
census.int <- subset(census, Census=="emergence")
## all species
m1 <- glm.nb(Phacelia~ Microsite * Nutrient * Site + Year, data=census.int)
anova(m1, test="Chisq") ## Site * Micro and Year significant## Analysis of Deviance Table
##
## Model: Negative Binomial(0.8108), link: log
##
## Response: Phacelia
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 839 1079.71
## Microsite 1 10.304 838 1069.41 0.001328 **
## Nutrient 1 1.234 837 1068.17 0.266538
## Site 6 159.651 831 908.52 < 2.2e-16 ***
## Year 1 31.424 830 877.10 2.073e-08 ***
## Microsite:Nutrient 1 0.046 829 877.05 0.829874
## Microsite:Site 6 21.032 823 856.02 0.001811 **
## Nutrient:Site 6 5.542 817 850.48 0.476389
## Microsite:Nutrient:Site 6 8.030 811 842.45 0.235879
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Phacelia
m1 <- glm.nb(Phacelia~ Microsite * Nutrient * Site + Year, data=census.int)
anova(m1, test="Chisq") ## Site * Micro and Year significant## Analysis of Deviance Table
##
## Model: Negative Binomial(0.8108), link: log
##
## Response: Phacelia
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 839 1079.71
## Microsite 1 10.304 838 1069.41 0.001328 **
## Nutrient 1 1.234 837 1068.17 0.266538
## Site 6 159.651 831 908.52 < 2.2e-16 ***
## Year 1 31.424 830 877.10 2.073e-08 ***
## Microsite:Nutrient 1 0.046 829 877.05 0.829874
## Microsite:Site 6 21.032 823 856.02 0.001811 **
## Nutrient:Site 6 5.542 817 850.48 0.476389
## Microsite:Nutrient:Site 6 8.030 811 842.45 0.235879
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Plantago
m2 <- glm.nb(Plantago~ Microsite * Nutrient * Site + Year, data=census.int)
anova(m2, test="Chisq") ## Site * Micro and Year significant## Analysis of Deviance Table
##
## Model: Negative Binomial(0.5335), link: log
##
## Response: Plantago
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 839 984.30
## Microsite 1 6.256 838 978.04 0.01238 *
## Nutrient 1 1.060 837 976.98 0.30332
## Site 6 162.687 831 814.30 < 2.2e-16 ***
## Year 1 4.138 830 810.16 0.04194 *
## Microsite:Nutrient 1 2.388 829 807.77 0.12224
## Microsite:Site 6 43.000 823 764.77 1.167e-07 ***
## Nutrient:Site 6 6.391 817 758.38 0.38088
## Microsite:Nutrient:Site 6 4.615 811 753.77 0.59403
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Salvia
m3 <- glm.nb(Salvia~ Microsite * Nutrient * Site + Year, data=census.int)
anova(m3, test="Chisq") ## Site * Micro and Year significant + (micro x nutrient)## Analysis of Deviance Table
##
## Model: Negative Binomial(0.8273), link: log
##
## Response: Salvia
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 839 1210.25
## Microsite 1 5.924 838 1204.33 0.0149386 *
## Nutrient 1 1.513 837 1202.82 0.2186678
## Site 6 211.625 831 991.19 < 2.2e-16 ***
## Year 1 17.463 830 973.73 2.929e-05 ***
## Microsite:Nutrient 1 7.824 829 965.90 0.0051548 **
## Microsite:Site 6 26.421 823 939.48 0.0001858 ***
## Nutrient:Site 6 4.587 817 934.90 0.5977624
## Microsite:Nutrient:Site 6 1.566 811 933.33 0.9549811
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1census.int.plot <- gather(census.int, species, abundance, Phacelia:Salvia)
##calculate confidence interval
census.plot<- census.int.plot %>% group_by(Microsite, species, Site, Year) %>% summarize(avg=mean(abundance),ci=se(abundance)*1.96)
ggplot(census.plot, aes(x=Microsite, y=avg, fill=species))+
geom_bar(position=position_dodge(), stat="identity")+
geom_errorbar(aes(ymin=avg-ci, ymax=avg+ci),
width=.2, # Width of the error bars
position=position_dodge(.9))+ scale_fill_brewer() +ylab("Abundance")+ theme_Publication() + facet_grid(Year~Site) 
## end season
census.end <- subset(census, Census=="end")
census.end[,"Year"] <- as.factor(census.end$Year)
## run full model for phyto biomass
m1 <- aov(log(phyto.biomass)~ Site * Microsite * Nutrient + Year, data=subset(census.end, phyto.biomass>0))
summary(m1)## Df Sum Sq Mean Sq F value Pr(>F)
## Site 6 330.8 55.13 32.164 < 2e-16 ***
## Microsite 1 10.9 10.88 6.347 0.012127 *
## Nutrient 1 30.3 30.25 17.651 3.24e-05 ***
## Year 1 161.2 161.21 94.051 < 2e-16 ***
## Site:Microsite 6 23.9 3.99 2.327 0.031940 *
## Site:Nutrient 6 41.9 6.98 4.073 0.000551 ***
## Microsite:Nutrient 1 1.7 1.73 1.011 0.315196
## Site:Microsite:Nutrient 6 7.9 1.32 0.768 0.595068
## Residuals 424 726.7 1.71
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(m1, "Microsite")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log(phyto.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, phyto.biomass > 0))
##
## $Microsite
## diff lwr upr p adj
## shrub-open 0.3096905 0.06750868 0.5518722 0.0123243## all species end of season
m2 <- glm.nb(phyto.abd~ Microsite * Nutrient * Site + Year, data=census.end)
anova(m2, test="Chisq") ## Site and microsite*nutrient significant## Analysis of Deviance Table
##
## Model: Negative Binomial(0.455), link: log
##
## Response: phyto.abd
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 839 1009.51
## Microsite 1 1.059 838 1008.45 0.30341
## Nutrient 1 2.527 837 1005.92 0.11194
## Site 6 135.420 831 870.50 < 2e-16 ***
## Year 1 3.412 830 867.09 0.06474 .
## Microsite:Nutrient 1 4.586 829 862.50 0.03224 *
## Microsite:Site 6 5.340 823 857.16 0.50104
## Nutrient:Site 6 5.169 817 852.00 0.52238
## Microsite:Nutrient:Site 6 5.683 811 846.31 0.45956
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## run full model for phacelia biomass
m3 <- aov(log(Phacelia.biomass)~ Site * Microsite * Nutrient + Year, data=subset(census.end, Phacelia.biomass>0))
summary(m3) ## site and microsite## Df Sum Sq Mean Sq F value Pr(>F)
## Site 6 89.7 14.95 8.274 4.95e-08 ***
## Microsite 1 21.2 21.20 11.738 0.00074 ***
## Nutrient 1 10.2 10.19 5.642 0.01845 *
## Year 1 95.5 95.49 52.857 7.42e-12 ***
## Site:Microsite 6 23.6 3.93 2.173 0.04692 *
## Site:Nutrient 6 8.0 1.34 0.742 0.61612
## Microsite:Nutrient 1 0.0 0.00 0.001 0.97552
## Site:Microsite:Nutrient 5 4.1 0.82 0.452 0.81122
## Residuals 205 370.3 1.81
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(m3, "Microsite")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log(Phacelia.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, Phacelia.biomass > 0))
##
## $Microsite
## diff lwr upr p adj
## shrub-open 0.6312547 0.2667053 0.9958041 0.0007714## run full model for plantago biomass
m4 <- aov(log(Plantago.biomass)~ Site * Microsite * Nutrient + Year, data=subset(census.end, Plantago.biomass>0))
summary(m4) ## site and year + sitexmicrosite## Df Sum Sq Mean Sq F value Pr(>F)
## Site 6 116.35 19.39 19.843 2.32e-16 ***
## Microsite 1 1.96 1.96 2.001 0.160
## Nutrient 1 32.03 32.03 32.778 6.80e-08 ***
## Year 1 138.38 138.38 141.597 < 2e-16 ***
## Site:Microsite 6 4.87 0.81 0.830 0.549
## Site:Nutrient 4 1.99 0.50 0.508 0.730
## Microsite:Nutrient 1 3.17 3.17 3.244 0.074 .
## Site:Microsite:Nutrient 3 0.65 0.22 0.220 0.882
## Residuals 130 127.05 0.98
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(m4, "Microsite")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log(Plantago.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, Plantago.biomass > 0))
##
## $Microsite
## diff lwr upr p adj
## shrub-open -0.2179629 -0.5348829 0.0989571 0.1759823## run full model for plantago biomass
m5 <- aov(log(Salvia.biomass)~ Site * Microsite * Nutrient + Year, data=subset(census.end, Salvia.biomass>0))
summary(m5) ## site and year + sitexmicrosite## Df Sum Sq Mean Sq F value Pr(>F)
## Site 6 170.1 28.35 19.982 < 2e-16 ***
## Microsite 1 0.0 0.01 0.008 0.929221
## Nutrient 1 21.4 21.43 15.103 0.000124 ***
## Year 1 44.1 44.05 31.045 5.39e-08 ***
## Site:Microsite 6 6.8 1.14 0.802 0.568988
## Site:Nutrient 6 46.2 7.70 5.424 2.35e-05 ***
## Microsite:Nutrient 1 0.5 0.55 0.387 0.534459
## Site:Microsite:Nutrient 6 12.9 2.16 1.520 0.171069
## Residuals 317 449.8 1.42
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(m5, "Microsite")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log(Salvia.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, Salvia.biomass > 0))
##
## $Microsite
## diff lwr upr p adj
## shrub-open 0.01133515 -0.2406604 0.2633307 0.9295351## run full model for phacelia abundance
m6 <- glm.nb(Phacelia.biomass~ Site * Microsite * Nutrient + Year, data=subset(census.end, Phacelia>0))
summary(m3) ## site and microsite## Df Sum Sq Mean Sq F value Pr(>F)
## Site 6 89.7 14.95 8.274 4.95e-08 ***
## Microsite 1 21.2 21.20 11.738 0.00074 ***
## Nutrient 1 10.2 10.19 5.642 0.01845 *
## Year 1 95.5 95.49 52.857 7.42e-12 ***
## Site:Microsite 6 23.6 3.93 2.173 0.04692 *
## Site:Nutrient 6 8.0 1.34 0.742 0.61612
## Microsite:Nutrient 1 0.0 0.00 0.001 0.97552
## Site:Microsite:Nutrient 5 4.1 0.82 0.452 0.81122
## Residuals 205 370.3 1.81
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(m3, "Microsite")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log(Phacelia.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, Phacelia.biomass > 0))
##
## $Microsite
## diff lwr upr p adj
## shrub-open 0.6312547 0.2667053 0.9958041 0.0007714## run full model for plantago biomass
m7 <- aov(log(Plantago)~ Site * Microsite * Nutrient + Year, data=subset(census.end, Plantago>0))
summary(m4) ## site and year + sitexmicrosite## Df Sum Sq Mean Sq F value Pr(>F)
## Site 6 116.35 19.39 19.843 2.32e-16 ***
## Microsite 1 1.96 1.96 2.001 0.160
## Nutrient 1 32.03 32.03 32.778 6.80e-08 ***
## Year 1 138.38 138.38 141.597 < 2e-16 ***
## Site:Microsite 6 4.87 0.81 0.830 0.549
## Site:Nutrient 4 1.99 0.50 0.508 0.730
## Microsite:Nutrient 1 3.17 3.17 3.244 0.074 .
## Site:Microsite:Nutrient 3 0.65 0.22 0.220 0.882
## Residuals 130 127.05 0.98
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(m4, "Microsite")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log(Plantago.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, Plantago.biomass > 0))
##
## $Microsite
## diff lwr upr p adj
## shrub-open -0.2179629 -0.5348829 0.0989571 0.1759823## run full model for plantago biomass
m8 <- aov(log(Salvia)~ Site * Microsite * Nutrient + Year, data=subset(census.end, Salvia>0))
summary(m5) ## site and year + sitexmicrosite## Df Sum Sq Mean Sq F value Pr(>F)
## Site 6 170.1 28.35 19.982 < 2e-16 ***
## Microsite 1 0.0 0.01 0.008 0.929221
## Nutrient 1 21.4 21.43 15.103 0.000124 ***
## Year 1 44.1 44.05 31.045 5.39e-08 ***
## Site:Microsite 6 6.8 1.14 0.802 0.568988
## Site:Nutrient 6 46.2 7.70 5.424 2.35e-05 ***
## Microsite:Nutrient 1 0.5 0.55 0.387 0.534459
## Site:Microsite:Nutrient 6 12.9 2.16 1.520 0.171069
## Residuals 317 449.8 1.42
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(m5, "Microsite")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log(Salvia.biomass) ~ Site * Microsite * Nutrient + Year, data = subset(census.end, Salvia.biomass > 0))
##
## $Microsite
## diff lwr upr p adj
## shrub-open 0.01133515 -0.2406604 0.2633307 0.9295351census.end.plot <- gather(census.end, species, biomass, Phacelia.biomass:Salvia.biomass)
##calculate confidence interval
census.plot<- census.end.plot %>% group_by(Microsite, species, Site, Year) %>% summarize(avg=mean(biomass),ci=se(biomass)*1.96)
ggplot(census.plot, aes(x=Microsite, y=avg, fill=species))+
geom_bar(position=position_dodge(), stat="identity")+
geom_errorbar(aes(ymin=avg-ci, ymax=avg+ci),
width=.2, # Width of the error bars
position=position_dodge(.9))+ scale_fill_brewer() +ylab("Abundance")+ theme_Publication() + facet_grid(Year~Site) 
### RDA
#
# ##collect environmental variables for site
# nutrients <- read.csv("Data/ERG.soilnutrients.csv")
# nutrients.mean <- nutrients %>% group_by(gradient,site, microsite) %>% summarise_each(funs(mean))
# nutrients.vars <- data.frame(nutrients.mean)
#
# ## minimum explanation of variables
# ambient2016[is.na(ambient2016)] <- 0
# community <- ambient2016 %>% group_by(Gradient,Site, Microsite) %>% summarise_each(funs(sum))
# community <- data.frame(community)
#
# end.census <- subset(census2016, census=="end")
# end.census <- end.census %>% group_by(Gradient,Site, Microsite) %>% summarise_each(funs(mean))
# end.census<- data.frame(end.census)
#
# ## daily means
# day.mean <- aggregate(HOBO.data, by=list(day=HOBO.data$Day, micro=HOBO.data$Microsite, site=HOBO.data$Site), mean)
#
# ## summarize daily means across sites
# means <- day.mean %>% group_by(gradient, micro) %>% summarize(temp=mean(Temp),rh=mean(RH),temp.se=se(Temp),rh.se=se(RH))
# means <- data.frame(means)
#
#
# envs <- data.frame(swc=end.census[,"swc"],nutrients.vars[,c("N","P","K")], means[,c("temp","rh")])
#
# ## hellinger transformation
# community.trans <- decostand(community[,12:42], "hellinger")
#
# ## rda with environmental variables
# rda1 <- rda(community.trans, envs)
# anova(rda1)
# (R2adj <- RsquareAdj(rda1)$adj.r.squared) ## 25.7% of variation explained
#
#
# ## build byplot manually
# par(mar=c(4.5,4.5,0.5,0.5))
# plot(rda1, type="n", xlim=c(-1,1))
#
# with(community, levels(Site))
#
# spp.priority <- colSums(community[,12:42])
#
# colvec <- c("blue","dodgerblue3","cyan","yellow2","orange","tomato","red2")
# with(community, points(rda1, display = "sites", col = "black", pch = c(21,22), bg = colvec[Site]))
# orditorp(rda1, display = "species", cex = 0.7, col = "darkred", priority=spp.priority, air=1,)
# text(rda1, display = "bp", col = "blue", cex = 0.8)
# legend("bottomright", pch=c(21), legend=community$Site[community$Microsite=="shrub"], pt.bg=colvec, cex=1)
#
# ## check variable explanation
# vif.cca(rda1)
#
# v.clim <- cbind(envs[,c("temp","rh")])
# v.nut <- cbind(envs[,c("N","P","K")])
# v.swc <- envs[,"swc"]
#
# x <- varpart(community.trans,v.clim,v.nut,v.swc)
#
# showvarparts(3)Difference in effect size
library(bootES)## Loading required package: boot##
## Attaching package: 'boot'## The following object is masked from 'package:lattice':
##
## melanomaeffect.cal <- function(x){ bootES(x, R=999, data.col="Biomass", group.col="Microsite", contrast=c(shrub=1,open=-1), effect.type=c("cohens.d"))}
effect.site <- by(subset(comm2016, Site != "Barstow"), comm2016$Site[comm2016$Site != "Barstow"], FUN=effect.cal)
site.summary <- rbind(summary(effect.site$Panoche),summary(effect.site$Cuyama),summary(effect.site$TejonRanch),data.frame(stat=0,ci.low=0,ci.high=0,bias=0,std.error=0),summary(effect.site$MojavePreserve),summary(effect.site$SheepholeValley),summary(effect.site$Tecopa))
site.summary[5,] <- 0
site.summary <- sapply(site.summary, as.numeric)
par(mar=c(4.5,4.5,.5,.5))
plot(c(1:7),site.summary[,"stat"], xlim=c(0.5,7.5), ylim=c(-1,3), pch=19, cex=1.4, ylab="Hedge's G", xlab="", xaxt="n")
arrows(c(1:7),as.numeric(site.summary[,"ci.high"]),c(1:7),site.summary[,"ci.low"], angle=90, code=3, length=0, lwd=2)
abline(h=0, lty=2, lwd=2)
axis(1, c(1:7), labels=site.names, cex.axis=1)
## phytometer
effect.cal <- function(x){ bootES(x, R=999, data.col="phyto.biomass", group.col="Microsite", contrast=c(shrub=1,open=-1), effect.type=c("cohens.d"))}
census2016 <- subset(census, Census=="end" & Year ==2016)
effect.site <- by(subset(census2016, Site != "Barstow"), census2016$Site[census2016$Site != "Barstow"], FUN=effect.cal)
site.summary <- rbind(summary(effect.site$Panoche),summary(effect.site$Cuyama),summary(effect.site$TejonRanch),data.frame(stat=0,ci.low=0,ci.high=0,bias=0,std.error=0),summary(effect.site$MojavePreserve),summary(effect.site$SheepholeValley),summary(effect.site$Tecopa))
site.summary <- sapply(site.summary, as.numeric)
par(mar=c(4.5,4.5,.5,.5))
plot(c(1:7),site.summary[,"stat"], xlim=c(0.5,7.5), ylim=c(-1,3), pch=19, cex=1.4, ylab="Hedge's G", xlab="", xaxt="n")
arrows(c(1:7),site.summary[,"ci.high"],c(1:7),site.summary[,"ci.low"], angle=90, code=3, length=0, lwd=2)
abline(h=0, lty=2, lwd=2)
axis(1, c(1:7), labels=site.names, cex.axis=1)
Rii between years
rii <- function(x, j, var)
{
s1 <- subset(x, Microsite == "shrub", select=var)
o1 <- subset(x, Microsite == "open", select=var)
return1 <- (s1 - o1) / (s1+o1)
x1 <- x[seq(1, nrow(x), by = 2),]
return2 <- cbind(x1[j], return1)
return2[is.na(return2)] <- 0
print(return2)
}
rii.dat <- rii(community, j=1:8, var=c("Abundance","Richness","Biomass"))## Year ID Site Rep Lat Long Elevation Gradient
## 1 2017 1 PanocheHills 1 36.70001 -120.8011 656.3209 1
## 3 2017 2 PanocheHills 2 36.70005 -120.8012 656.1902 1
## 5 2017 3 PanocheHills 3 36.70010 -120.8015 656.6475 1
## 7 2017 4 PanocheHills 4 36.70012 -120.8014 657.3752 1
## 9 2017 5 PanocheHills 5 36.70019 -120.8016 656.5075 1
## 11 2017 6 PanocheHills 6 36.70026 -120.8014 655.5837 1
## 13 2017 7 PanocheHills 7 36.70037 -120.8014 655.4344 1
## 15 2017 8 PanocheHills 8 36.70045 -120.8015 655.3132 1
## 17 2017 9 PanocheHills 9 36.70052 -120.8015 655.0519 1
## 19 2017 10 PanocheHills 10 36.70044 -120.8016 655.5371 1
## 21 2017 11 PanocheHills 11 36.70057 -120.8017 656.4329 1
## 23 2017 12 PanocheHills 12 36.70063 -120.8019 657.0300 1
## 25 2017 13 PanocheHills 13 36.70060 -120.8020 657.1606 1
## 27 2017 14 PanocheHills 14 36.70064 -120.8017 656.7687 1
## 29 2017 15 PanocheHills 15 36.70064 -120.8022 657.2166 1
## 31 2017 16 PanocheHills 16 36.70074 -120.8022 656.9554 1
## 33 2017 17 PanocheHills 17 36.70086 -120.8022 656.0223 1
## 35 2017 18 PanocheHills 18 36.70080 -120.8023 656.5541 1
## 37 2017 19 PanocheHills 19 36.70073 -120.8024 656.1996 1
## 39 2017 20 PanocheHills 20 36.70069 -120.8023 655.4438 1
## 41 2017 21 PanocheHills 21 36.70053 -120.8023 655.8543 1
## 43 2017 22 PanocheHills 22 36.70048 -120.8021 657.1420 1
## 45 2017 23 PanocheHills 23 36.70042 -120.8021 657.7485 1
## 47 2017 24 PanocheHills 24 36.70036 -120.8020 658.2523 1
## 49 2017 25 PanocheHills 25 36.70035 -120.8019 658.7189 1
## 51 2017 26 PanocheHills 26 36.70026 -120.8020 658.9055 1
## 53 2017 27 PanocheHills 27 36.70013 -120.8020 658.9988 1
## 55 2017 28 PanocheHills 28 36.70015 -120.8020 659.0361 1
## 57 2017 29 PanocheHills 29 36.70009 -120.8019 658.9614 1
## 59 2017 30 PanocheHills 30 36.70005 -120.8019 659.1201 1
## 61 2017 31 Cuyama 1 34.85522 -119.4885 806.8343 2
## 63 2017 32 Cuyama 2 34.85518 -119.4886 806.5264 2
## 65 2017 33 Cuyama 3 34.85512 -119.4886 806.4984 2
## 67 2017 34 Cuyama 4 34.85506 -119.4885 806.6196 2
## 69 2017 35 Cuyama 5 34.85505 -119.4884 807.0115 2
## 71 2017 36 Cuyama 6 34.85500 -119.4884 806.0505 2
## 73 2017 37 Cuyama 7 34.85489 -119.4885 808.0753 2
## 75 2017 38 Cuyama 8 34.85488 -119.4884 808.7097 2
## 77 2017 39 Cuyama 9 34.85489 -119.4883 808.5231 2
## 79 2017 40 Cuyama 10 34.85476 -119.4884 809.9227 2
## 81 2017 41 Cuyama 11 34.85462 -119.4885 809.3256 2
## 83 2017 42 Cuyama 12 34.85465 -119.4884 809.5961 2
## 85 2017 43 Cuyama 13 34.85454 -119.4884 810.1840 2
## 87 2017 44 Cuyama 14 34.85442 -119.4884 809.8107 2
## 89 2017 45 Cuyama 15 34.85441 -119.4882 810.0720 2
## 91 2017 46 Cuyama 16 34.85436 -119.4881 810.3986 2
## 93 2017 47 Cuyama 17 34.85443 -119.4880 810.0161 2
## 95 2017 48 Cuyama 18 34.85449 -119.4880 811.1917 2
## 97 2017 49 Cuyama 19 34.85461 -119.4879 811.6675 2
## 99 2017 50 Cuyama 20 34.85472 -119.4878 809.8854 2
## 101 2017 51 Cuyama 21 34.85472 -119.4877 810.4172 2
## 103 2017 52 Cuyama 22 34.85484 -119.4876 811.3410 2
## 105 2017 53 Cuyama 23 34.85492 -119.4874 810.9584 2
## 107 2017 54 Cuyama 24 34.85496 -119.4873 811.6302 2
## 109 2017 55 Cuyama 25 34.85500 -119.4874 811.9008 2
## 111 2017 56 Cuyama 26 34.85510 -119.4875 812.1807 2
## 113 2017 57 Cuyama 27 34.85511 -119.4876 812.1248 2
## 115 2017 58 Cuyama 28 34.85508 -119.4876 812.6193 2
## 117 2017 59 Cuyama 29 34.85499 -119.4879 811.4623 2
## 119 2017 60 Cuyama 30 34.85502 -119.4881 811.7795 2
## 121 2017 61 Barstow 1 35.09405 -116.8349 496.0200 4
## 123 2017 62 Barstow 2 35.09391 -116.8348 496.1690 4
## 125 2017 63 Barstow 3 35.09381 -116.8349 495.4130 4
## 127 2017 64 Barstow 4 35.09373 -116.8350 495.4600 4
## 129 2017 65 Barstow 5 35.09372 -116.8350 494.9840 4
## 131 2017 66 Barstow 6 35.09361 -116.8350 494.7600 4
## 133 2017 67 Barstow 7 35.09359 -116.8351 495.0870 4
## 135 2017 68 Barstow 8 35.09358 -116.8351 495.1050 4
## 137 2017 69 Barstow 9 35.09353 -116.8351 495.1520 4
## 139 2017 70 Barstow 10 35.09342 -116.8350 494.9280 4
## 141 2017 71 Barstow 11 35.09338 -116.8352 494.8630 4
## 143 2017 72 Barstow 12 35.09336 -116.8351 494.9000 4
## 145 2017 73 Barstow 13 35.09332 -116.8351 494.8910 4
## 147 2017 74 Barstow 14 35.09325 -116.8352 494.5830 4
## 149 2017 75 Barstow 15 35.09318 -116.8352 495.0680 4
## 151 2017 76 Barstow 16 35.09313 -116.8352 494.8810 4
## 153 2017 77 Barstow 17 35.09310 -116.8353 494.6010 4
## 155 2017 78 Barstow 18 35.09299 -116.8352 494.1160 4
## 157 2017 79 Barstow 19 35.09289 -116.8352 493.9670 4
## 159 2017 80 Barstow 20 35.09276 -116.8352 493.7340 4
## 161 2017 81 Barstow 21 35.09275 -116.8351 573.0444 4
## 163 2017 82 Barstow 22 35.09271 -116.8350 572.1393 4
## 165 2017 83 Barstow 23 35.09280 -116.8349 572.9885 4
## 167 2017 84 Barstow 24 35.09275 -116.8348 572.3166 4
## 169 2017 85 Barstow 25 35.09280 -116.8347 571.7568 4
## 171 2017 86 Barstow 26 35.09287 -116.8348 571.9061 4
## 173 2017 87 Barstow 27 35.09290 -116.8346 571.6915 4
## 175 2017 88 Barstow 28 35.09295 -116.8345 571.9341 4
## 177 2017 89 Barstow 29 35.09302 -116.8347 571.4862 4
## 179 2017 90 Barstow 30 35.09311 -116.8347 571.7195 4
## 181 2017 91 HeartofMojave 1 34.69820 -115.6842 784.7300 5
## 183 2017 92 HeartofMojave 2 34.69811 -115.6841 784.6460 5
## 185 2017 93 HeartofMojave 3 34.69805 -115.6841 784.8510 5
## 187 2017 94 HeartofMojave 4 34.69798 -115.6841 784.8330 5
## 189 2017 95 HeartofMojave 5 34.69792 -115.6841 784.5530 5
## 191 2017 96 HeartofMojave 6 34.69794 -115.6843 784.2630 5
## 193 2017 97 HeartofMojave 7 34.69789 -115.6843 784.3660 5
## 195 2017 98 HeartofMojave 8 34.69784 -115.6844 784.2350 5
## 197 2017 99 HeartofMojave 9 34.69782 -115.6843 783.9930 5
## 199 2017 100 HeartofMojave 10 34.69779 -115.6844 783.2460 5
## 201 2017 101 HeartofMojave 11 34.69776 -115.6843 783.0600 5
## 203 2017 102 HeartofMojave 12 34.69772 -115.6844 783.0410 5
## 205 2017 103 HeartofMojave 13 34.69762 -115.6844 783.1720 5
## 207 2017 104 HeartofMojave 14 34.69764 -115.6845 782.6680 5
## 209 2017 105 HeartofMojave 15 34.69756 -115.6844 782.5840 5
## 211 2017 106 HeartofMojave 16 34.69747 -115.6843 781.8750 5
## 213 2017 107 HeartofMojave 17 34.69735 -115.6843 783.0130 5
## 215 2017 108 HeartofMojave 18 34.69730 -115.6843 782.3690 5
## 217 2017 109 HeartofMojave 19 34.69718 -115.6843 781.3430 5
## 219 2017 110 HeartofMojave 20 34.69714 -115.6843 781.0820 5
## 221 2017 111 HeartofMojave 21 34.69721 -115.6842 770.7805 5
## 223 2017 112 HeartofMojave 22 34.69729 -115.6841 770.9671 5
## 225 2017 113 HeartofMojave 23 34.69745 -115.6838 773.9343 5
## 227 2017 114 HeartofMojave 24 34.69748 -115.6838 773.0945 5
## 229 2017 115 HeartofMojave 25 34.69748 -115.6837 773.9996 5
## 231 2017 116 HeartofMojave 26 34.69755 -115.6837 774.5034 5
## 233 2017 117 HeartofMojave 27 34.69761 -115.6836 774.8766 5
## 235 2017 118 HeartofMojave 28 34.69764 -115.6836 775.0259 5
## 237 2017 119 HeartofMojave 29 34.69769 -115.6837 774.9606 5
## 239 2017 120 HeartofMojave 30 34.69772 -115.6837 775.0726 5
## 241 2017 121 SheepholeValley 1 34.20568 -115.7197 545.9200 6
## 243 2017 122 SheepholeValley 2 34.20574 -115.7195 546.5730 6
## 245 2017 123 SheepholeValley 3 34.20565 -115.7194 546.6290 6
## 247 2017 124 SheepholeValley 4 34.20559 -115.7192 546.2470 6
## 249 2017 125 SheepholeValley 5 34.20563 -115.7192 546.5170 6
## 251 2017 126 SheepholeValley 6 34.20558 -115.7190 546.2930 6
## 253 2017 127 SheepholeValley 7 34.20561 -115.7190 545.8920 6
## 255 2017 128 SheepholeValley 8 34.20555 -115.7190 545.7430 6
## 257 2017 129 SheepholeValley 9 34.20572 -115.7188 546.5080 6
## 259 2017 130 SheepholeValley 10 34.20577 -115.7187 546.9090 6
## 261 2017 131 SheepholeValley 11 34.20580 -115.7186 547.1330 6
## 263 2017 132 SheepholeValley 12 34.20594 -115.7186 547.8890 6
## 265 2017 133 SheepholeValley 13 34.20611 -115.7187 548.4580 6
## 267 2017 134 SheepholeValley 14 34.20626 -115.7190 549.5310 6
## 269 2017 135 SheepholeValley 15 34.20632 -115.7192 550.0070 6
## 271 2017 136 SheepholeValley 16 34.20631 -115.7194 550.3800 6
## 273 2017 137 SheepholeValley 17 34.20628 -115.7196 549.9420 6
## 275 2017 138 SheepholeValley 18 34.20625 -115.7196 550.1560 6
## 277 2017 139 SheepholeValley 19 34.20627 -115.7197 549.4560 6
## 279 2017 140 SheepholeValley 20 34.20618 -115.7198 549.1950 6
## 281 2017 141 SheepholeValley 21 34.20650 -115.7194 598.3586 6
## 283 2017 142 SheepholeValley 22 34.20652 -115.7192 597.8641 6
## 285 2017 143 SheepholeValley 23 34.20664 -115.7191 599.4130 6
## 287 2017 144 SheepholeValley 24 34.20678 -115.7192 600.1594 6
## 289 2017 145 SheepholeValley 25 34.20664 -115.7192 597.9014 6
## 291 2017 146 SheepholeValley 26 34.20659 -115.7193 597.4069 6
## 293 2017 147 SheepholeValley 27 34.20659 -115.7195 595.8207 6
## 295 2017 148 SheepholeValley 28 34.20658 -115.7198 595.4101 6
## 297 2017 149 SheepholeValley 29 34.20655 -115.7199 595.5687 6
## 299 2017 150 SheepholeValley 30 34.20661 -115.7199 596.3432 6
## 301 2017 151 Tecopa 1 35.85152 -116.1867 699.4567 7
## 303 2017 152 Tecopa 2 35.85145 -116.1867 699.5779 7
## 305 2017 153 Tecopa 3 35.85141 -116.1867 700.2591 7
## 307 2017 154 Tecopa 4 35.85134 -116.1866 700.8936 7
## 309 2017 155 Tecopa 5 35.85130 -116.1866 700.1472 7
## 311 2017 156 Tecopa 6 35.85127 -116.1866 699.6806 7
## 313 2017 157 Tecopa 7 35.85121 -116.1866 700.7536 7
## 315 2017 158 Tecopa 8 35.85118 -116.1865 700.9216 7
## 317 2017 159 Tecopa 9 35.85117 -116.1864 702.2092 7
## 319 2017 160 Tecopa 10 35.85127 -116.1863 702.9650 7
## 321 2017 161 Tecopa 11 35.85123 -116.1862 702.8064 7
## 323 2017 162 Tecopa 12 35.85118 -116.1863 704.1033 7
## 325 2017 163 Tecopa 13 35.85115 -116.1864 704.3740 7
## 327 2017 164 Tecopa 14 35.85108 -116.1865 705.4656 7
## 329 2017 165 Tecopa 15 35.85108 -116.1866 705.0178 7
## 331 2017 166 Tecopa 16 35.85102 -116.1866 706.5200 7
## 333 2017 167 Tecopa 17 35.85116 -116.1867 706.6413 7
## 335 2017 168 Tecopa 18 35.85123 -116.1867 707.5930 7
## 337 2017 169 Tecopa 19 35.85124 -116.1868 707.6583 7
## 339 2017 170 Tecopa 20 35.85130 -116.1869 445.7271 7
## 341 2017 171 Tecopa 21 35.85133 -116.1868 446.2497 7
## 343 2017 172 Tecopa 22 35.85140 -116.1869 447.1734 7
## 345 2017 173 Tecopa 23 35.85145 -116.1869 447.4533 7
## 347 2017 174 Tecopa 24 35.85146 -116.1868 448.7690 7
## 349 2017 175 Tecopa 25 35.85150 -116.1868 448.7130 7
## 351 2017 176 Tecopa 26 35.85152 -116.1869 449.0116 7
## 353 2017 177 Tecopa 27 35.85147 -116.1869 447.8639 7
## 355 2017 178 Tecopa 28 35.85153 -116.1870 449.0209 7
## 357 2017 179 Tecopa 29 35.85168 -116.1870 448.9182 7
## 359 2017 180 Tecopa 30 35.85101 -116.1872 449.0209 7
## 361 2017 181 TejonRanch 1 34.87599 -118.6025 1118.0220 3
## 363 2017 182 TejonRanch 2 34.87595 -118.6025 1118.0500 3
## 365 2017 183 TejonRanch 3 34.87593 -118.6025 1117.6300 3
## 367 2017 184 TejonRanch 4 34.87593 -118.6025 1117.5090 3
## 369 2017 185 TejonRanch 5 34.87589 -118.6025 1117.4340 3
## 371 2017 186 TejonRanch 6 34.87584 -118.6025 1117.3410 3
## 373 2017 187 TejonRanch 7 34.87583 -118.6024 1117.2010 3
## 375 2017 188 TejonRanch 8 34.87582 -118.6024 1116.5110 3
## 377 2017 189 TejonRanch 9 34.87574 -118.6023 1115.8020 3
## 379 2017 190 TejonRanch 10 34.87552 -118.6022 1114.5140 3
## 381 2017 191 TejonRanch 11 34.87600 -118.6027 1116.3710 3
## 383 2017 192 TejonRanch 12 34.87607 -118.6027 1116.9490 3
## 385 2017 193 TejonRanch 13 34.87604 -118.6028 1117.4340 3
## 387 2017 194 TejonRanch 14 34.87608 -118.6028 1117.6300 3
## 389 2017 195 TejonRanch 15 34.87612 -118.6026 1117.1640 3
## 391 2017 196 TejonRanch 16 34.87618 -118.6027 1117.7140 3
## 393 2017 197 TejonRanch 17 34.87619 -118.6026 1117.7420 3
## 395 2017 198 TejonRanch 18 34.87619 -118.6026 1118.2090 3
## 397 2017 199 TejonRanch 19 34.87620 -118.6026 1118.0880 3
## 399 2017 200 TejonRanch 20 34.87606 -118.6026 1117.5560 3
## 401 2017 201 TejonRanch 21 34.87646 -118.6018 1117.7609 3
## 403 2017 202 TejonRanch 22 34.87641 -118.6017 1116.6505 3
## 405 2017 203 TejonRanch 23 34.87638 -118.6018 1117.1451 3
## 407 2017 204 TejonRanch 24 34.87639 -118.6018 1117.3970 3
## 409 2017 205 TejonRanch 25 34.87640 -118.6018 1117.1824 3
## 411 2017 206 TejonRanch 26 34.87633 -118.6019 1115.8668 3
## 413 2017 207 TejonRanch 27 34.87628 -118.6019 1116.7532 3
## 415 2017 208 TejonRanch 28 34.87625 -118.6020 1116.4640 3
## 417 2017 209 TejonRanch 29 34.87627 -118.6020 1116.5480 3
## 419 2017 210 TejonRanch 30 34.87623 -118.6020 1117.2290 3
## 421 2016 1 PanocheHills 1 36.70001 -120.8011 656.3209 1
## 423 2016 2 PanocheHills 2 36.70005 -120.8012 656.1902 1
## 425 2016 3 PanocheHills 3 36.70010 -120.8015 656.6475 1
## 427 2016 4 PanocheHills 4 36.70012 -120.8014 657.3752 1
## 429 2016 5 PanocheHills 5 36.70019 -120.8016 656.5075 1
## 431 2016 6 PanocheHills 6 36.70026 -120.8014 655.5837 1
## 433 2016 7 PanocheHills 7 36.70037 -120.8014 655.4344 1
## 435 2016 8 PanocheHills 8 36.70045 -120.8015 655.3132 1
## 437 2016 9 PanocheHills 9 36.70052 -120.8015 655.0519 1
## 439 2016 10 PanocheHills 10 36.70044 -120.8016 655.5371 1
## 441 2016 11 PanocheHills 11 36.70057 -120.8017 656.4329 1
## 443 2016 12 PanocheHills 12 36.70063 -120.8019 657.0300 1
## 445 2016 13 PanocheHills 13 36.70060 -120.8020 657.1606 1
## 447 2016 14 PanocheHills 14 36.70064 -120.8017 656.7687 1
## 449 2016 15 PanocheHills 15 36.70064 -120.8022 657.2166 1
## 451 2016 16 PanocheHills 16 36.70074 -120.8022 656.9554 1
## 453 2016 17 PanocheHills 17 36.70086 -120.8022 656.0223 1
## 455 2016 18 PanocheHills 18 36.70080 -120.8023 656.5541 1
## 457 2016 19 PanocheHills 19 36.70073 -120.8024 656.1996 1
## 459 2016 20 PanocheHills 20 36.70069 -120.8023 655.4438 1
## 461 2016 21 PanocheHills 21 36.70053 -120.8023 655.8543 1
## 463 2016 22 PanocheHills 22 36.70048 -120.8021 657.1420 1
## 465 2016 23 PanocheHills 23 36.70042 -120.8021 657.7485 1
## 467 2016 24 PanocheHills 24 36.70036 -120.8020 658.2523 1
## 469 2016 25 PanocheHills 25 36.70035 -120.8019 658.7189 1
## 471 2016 26 PanocheHills 26 36.70026 -120.8020 658.9055 1
## 473 2016 27 PanocheHills 27 36.70013 -120.8020 658.9988 1
## 475 2016 28 PanocheHills 28 36.70015 -120.8020 659.0361 1
## 477 2016 29 PanocheHills 29 36.70009 -120.8019 658.9614 1
## 479 2016 30 PanocheHills 30 36.70005 -120.8019 659.1201 1
## 481 2016 31 Cuyama 1 34.85522 -119.4885 806.8343 2
## 483 2016 32 Cuyama 2 34.85518 -119.4886 806.5264 2
## 485 2016 33 Cuyama 3 34.85512 -119.4886 806.4984 2
## 487 2016 34 Cuyama 4 34.85506 -119.4885 806.6196 2
## 489 2016 35 Cuyama 5 34.85505 -119.4884 807.0115 2
## 491 2016 36 Cuyama 6 34.85500 -119.4884 806.0505 2
## 493 2016 37 Cuyama 7 34.85489 -119.4885 808.0753 2
## 495 2016 38 Cuyama 8 34.85488 -119.4884 808.7097 2
## 497 2016 39 Cuyama 9 34.85489 -119.4883 808.5231 2
## 499 2016 40 Cuyama 10 34.85476 -119.4884 809.9227 2
## 501 2016 41 Cuyama 11 34.85462 -119.4885 809.3256 2
## 503 2016 42 Cuyama 12 34.85465 -119.4884 809.5961 2
## 505 2016 43 Cuyama 13 34.85454 -119.4884 810.1840 2
## 507 2016 44 Cuyama 14 34.85442 -119.4884 809.8107 2
## 509 2016 45 Cuyama 15 34.85441 -119.4882 810.0720 2
## 511 2016 46 Cuyama 16 34.85436 -119.4881 810.3986 2
## 513 2016 47 Cuyama 17 34.85443 -119.4880 810.0161 2
## 515 2016 48 Cuyama 18 34.85449 -119.4880 811.1917 2
## 517 2016 49 Cuyama 19 34.85461 -119.4879 811.6675 2
## 519 2016 50 Cuyama 20 34.85472 -119.4878 809.8854 2
## 521 2016 51 Cuyama 21 34.85472 -119.4877 810.4172 2
## 523 2016 52 Cuyama 22 34.85484 -119.4876 811.3410 2
## 525 2016 53 Cuyama 23 34.85492 -119.4874 810.9584 2
## 527 2016 54 Cuyama 24 34.85496 -119.4873 811.6302 2
## 529 2016 55 Cuyama 25 34.85500 -119.4874 811.9008 2
## 531 2016 56 Cuyama 26 34.85510 -119.4875 812.1807 2
## 533 2016 57 Cuyama 27 34.85511 -119.4876 812.1248 2
## 535 2016 58 Cuyama 28 34.85508 -119.4876 812.6193 2
## 537 2016 59 Cuyama 29 34.85499 -119.4879 811.4623 2
## 539 2016 60 Cuyama 30 34.85502 -119.4881 811.7795 2
## 541 2016 61 Barstow 1 35.09405 -116.8349 496.0200 4
## 543 2016 62 Barstow 2 35.09391 -116.8348 496.1690 4
## 545 2016 63 Barstow 3 35.09381 -116.8349 495.4130 4
## 547 2016 64 Barstow 4 35.09373 -116.8350 495.4600 4
## 549 2016 65 Barstow 5 35.09372 -116.8350 494.9840 4
## 551 2016 66 Barstow 6 35.09361 -116.8350 494.7600 4
## 553 2016 67 Barstow 7 35.09359 -116.8351 495.0870 4
## 555 2016 68 Barstow 8 35.09358 -116.8351 495.1050 4
## 557 2016 69 Barstow 9 35.09353 -116.8351 495.1520 4
## 559 2016 70 Barstow 10 35.09342 -116.8350 494.9280 4
## 561 2016 71 Barstow 11 35.09338 -116.8352 494.8630 4
## 563 2016 72 Barstow 12 35.09336 -116.8351 494.9000 4
## 565 2016 73 Barstow 13 35.09332 -116.8351 494.8910 4
## 567 2016 74 Barstow 14 35.09325 -116.8352 494.5830 4
## 569 2016 75 Barstow 15 35.09318 -116.8352 495.0680 4
## 571 2016 76 Barstow 16 35.09313 -116.8352 494.8810 4
## 573 2016 77 Barstow 17 35.09310 -116.8353 494.6010 4
## 575 2016 78 Barstow 18 35.09299 -116.8352 494.1160 4
## 577 2016 79 Barstow 19 35.09289 -116.8352 493.9670 4
## 579 2016 80 Barstow 20 35.09276 -116.8352 493.7340 4
## 581 2016 81 Barstow 21 35.09275 -116.8351 573.0444 4
## 583 2016 82 Barstow 22 35.09271 -116.8350 572.1393 4
## 585 2016 83 Barstow 23 35.09280 -116.8349 572.9885 4
## 587 2016 84 Barstow 24 35.09275 -116.8348 572.3166 4
## 589 2016 85 Barstow 25 35.09280 -116.8347 571.7568 4
## 591 2016 86 Barstow 26 35.09287 -116.8348 571.9061 4
## 593 2016 87 Barstow 27 35.09290 -116.8346 571.6915 4
## 595 2016 88 Barstow 28 35.09295 -116.8345 571.9341 4
## 597 2016 89 Barstow 29 35.09302 -116.8347 571.4862 4
## 599 2016 90 Barstow 30 35.09311 -116.8347 571.7195 4
## 601 2016 91 HeartofMojave 1 34.69820 -115.6842 784.7300 5
## 603 2016 92 HeartofMojave 2 34.69811 -115.6841 784.6460 5
## 605 2016 93 HeartofMojave 3 34.69805 -115.6841 784.8510 5
## 607 2016 94 HeartofMojave 4 34.69798 -115.6841 784.8330 5
## 609 2016 95 HeartofMojave 5 34.69792 -115.6841 784.5530 5
## 611 2016 96 HeartofMojave 6 34.69794 -115.6843 784.2630 5
## 613 2016 97 HeartofMojave 7 34.69789 -115.6843 784.3660 5
## 615 2016 98 HeartofMojave 8 34.69784 -115.6844 784.2350 5
## 617 2016 99 HeartofMojave 9 34.69782 -115.6843 783.9930 5
## 619 2016 100 HeartofMojave 10 34.69779 -115.6844 783.2460 5
## 621 2016 101 HeartofMojave 11 34.69776 -115.6843 783.0600 5
## 623 2016 102 HeartofMojave 12 34.69772 -115.6844 783.0410 5
## 625 2016 103 HeartofMojave 13 34.69762 -115.6844 783.1720 5
## 627 2016 104 HeartofMojave 14 34.69764 -115.6845 782.6680 5
## 629 2016 105 HeartofMojave 15 34.69756 -115.6844 782.5840 5
## 631 2016 106 HeartofMojave 16 34.69747 -115.6843 781.8750 5
## 633 2016 107 HeartofMojave 17 34.69735 -115.6843 783.0130 5
## 635 2016 108 HeartofMojave 18 34.69730 -115.6843 782.3690 5
## 637 2016 109 HeartofMojave 19 34.69718 -115.6843 781.3430 5
## 639 2016 110 HeartofMojave 20 34.69714 -115.6843 781.0820 5
## 641 2016 111 HeartofMojave 21 34.69721 -115.6842 770.7805 5
## 643 2016 112 HeartofMojave 22 34.69729 -115.6841 770.9671 5
## 645 2016 113 HeartofMojave 23 34.69745 -115.6838 773.9343 5
## 647 2016 114 HeartofMojave 24 34.69748 -115.6838 773.0945 5
## 649 2016 115 HeartofMojave 25 34.69748 -115.6837 773.9996 5
## 651 2016 116 HeartofMojave 26 34.69755 -115.6837 774.5034 5
## 653 2016 117 HeartofMojave 27 34.69761 -115.6836 774.8766 5
## 655 2016 118 HeartofMojave 28 34.69764 -115.6836 775.0259 5
## 657 2016 119 HeartofMojave 29 34.69769 -115.6837 774.9606 5
## 659 2016 120 HeartofMojave 30 34.69772 -115.6837 775.0726 5
## 661 2016 121 SheepholeValley 1 34.20568 -115.7197 545.9200 6
## 663 2016 122 SheepholeValley 2 34.20574 -115.7195 546.5730 6
## 665 2016 123 SheepholeValley 3 34.20565 -115.7194 546.6290 6
## 667 2016 124 SheepholeValley 4 34.20559 -115.7192 546.2470 6
## 669 2016 125 SheepholeValley 5 34.20563 -115.7192 546.5170 6
## 671 2016 126 SheepholeValley 6 34.20558 -115.7190 546.2930 6
## 673 2016 127 SheepholeValley 7 34.20561 -115.7190 545.8920 6
## 675 2016 128 SheepholeValley 8 34.20555 -115.7190 545.7430 6
## 677 2016 129 SheepholeValley 9 34.20572 -115.7188 546.5080 6
## 679 2016 130 SheepholeValley 10 34.20577 -115.7187 546.9090 6
## 681 2016 131 SheepholeValley 11 34.20580 -115.7186 547.1330 6
## 683 2016 132 SheepholeValley 12 34.20594 -115.7186 547.8890 6
## 685 2016 133 SheepholeValley 13 34.20611 -115.7187 548.4580 6
## 687 2016 134 SheepholeValley 14 34.20626 -115.7190 549.5310 6
## 689 2016 135 SheepholeValley 15 34.20632 -115.7192 550.0070 6
## 691 2016 136 SheepholeValley 16 34.20631 -115.7194 550.3800 6
## 693 2016 137 SheepholeValley 17 34.20628 -115.7196 549.9420 6
## 695 2016 138 SheepholeValley 18 34.20625 -115.7196 550.1560 6
## 697 2016 139 SheepholeValley 19 34.20627 -115.7197 549.4560 6
## 699 2016 140 SheepholeValley 20 34.20618 -115.7198 549.1950 6
## 701 2016 141 SheepholeValley 21 34.20650 -115.7194 598.3586 6
## 703 2016 142 SheepholeValley 22 34.20652 -115.7192 597.8641 6
## 705 2016 143 SheepholeValley 23 34.20664 -115.7191 599.4130 6
## 707 2016 144 SheepholeValley 24 34.20678 -115.7192 600.1594 6
## 709 2016 145 SheepholeValley 25 34.20664 -115.7192 597.9014 6
## 711 2016 146 SheepholeValley 26 34.20659 -115.7193 597.4069 6
## 713 2016 147 SheepholeValley 27 34.20659 -115.7195 595.8207 6
## 715 2016 148 SheepholeValley 28 34.20658 -115.7198 595.4101 6
## 717 2016 149 SheepholeValley 29 34.20655 -115.7199 595.5687 6
## 719 2016 150 SheepholeValley 30 34.20661 -115.7199 596.3432 6
## 721 2016 151 Tecopa 1 35.85152 -116.1867 699.4567 7
## 723 2016 152 Tecopa 2 35.85145 -116.1867 699.5779 7
## 725 2016 153 Tecopa 3 35.85141 -116.1867 700.2591 7
## 727 2016 154 Tecopa 4 35.85134 -116.1866 700.8936 7
## 729 2016 155 Tecopa 5 35.85130 -116.1866 700.1472 7
## 731 2016 156 Tecopa 6 35.85127 -116.1866 699.6806 7
## 733 2016 157 Tecopa 7 35.85121 -116.1866 700.7536 7
## 735 2016 158 Tecopa 8 35.85118 -116.1865 700.9216 7
## 737 2016 159 Tecopa 9 35.85117 -116.1864 702.2092 7
## 739 2016 160 Tecopa 10 35.85127 -116.1863 702.9650 7
## 741 2016 161 Tecopa 11 35.85123 -116.1862 702.8064 7
## 743 2016 162 Tecopa 12 35.85118 -116.1863 704.1033 7
## 745 2016 163 Tecopa 13 35.85115 -116.1864 704.3740 7
## 747 2016 164 Tecopa 14 35.85108 -116.1865 705.4656 7
## 749 2016 165 Tecopa 15 35.85108 -116.1866 705.0178 7
## 751 2016 166 Tecopa 16 35.85102 -116.1866 706.5200 7
## 753 2016 167 Tecopa 17 35.85116 -116.1867 706.6413 7
## 755 2016 168 Tecopa 18 35.85123 -116.1867 707.5930 7
## 757 2016 169 Tecopa 19 35.85124 -116.1868 707.6583 7
## 759 2016 170 Tecopa 20 35.85130 -116.1869 445.7271 7
## 761 2016 171 Tecopa 21 35.85133 -116.1868 446.2497 7
## 763 2016 172 Tecopa 22 35.85140 -116.1869 447.1734 7
## 765 2016 173 Tecopa 23 35.85145 -116.1869 447.4533 7
## 767 2016 174 Tecopa 24 35.85146 -116.1868 448.7690 7
## 769 2016 175 Tecopa 25 35.85150 -116.1868 448.7130 7
## 771 2016 176 Tecopa 26 35.85152 -116.1869 449.0116 7
## 773 2016 177 Tecopa 27 35.85147 -116.1869 447.8639 7
## 775 2016 178 Tecopa 28 35.85153 -116.1870 449.0209 7
## 777 2016 179 Tecopa 29 35.85168 -116.1870 448.9182 7
## 779 2016 180 Tecopa 30 35.85101 -116.1872 449.0209 7
## 781 2016 181 TejonRanch 1 34.87599 -118.6025 1118.0220 3
## 783 2016 182 TejonRanch 2 34.87595 -118.6025 1118.0500 3
## 785 2016 183 TejonRanch 3 34.87593 -118.6025 1117.6300 3
## 787 2016 184 TejonRanch 4 34.87593 -118.6025 1117.5090 3
## 789 2016 185 TejonRanch 5 34.87589 -118.6025 1117.4340 3
## 791 2016 186 TejonRanch 6 34.87584 -118.6025 1117.3410 3
## 793 2016 187 TejonRanch 7 34.87583 -118.6024 1117.2010 3
## 795 2016 188 TejonRanch 8 34.87582 -118.6024 1116.5110 3
## 797 2016 189 TejonRanch 9 34.87574 -118.6023 1115.8020 3
## 799 2016 190 TejonRanch 10 34.87552 -118.6022 1114.5140 3
## 801 2016 191 TejonRanch 11 34.87600 -118.6027 1116.3710 3
## 803 2016 192 TejonRanch 12 34.87607 -118.6027 1116.9490 3
## 805 2016 193 TejonRanch 13 34.87604 -118.6028 1117.4340 3
## 807 2016 194 TejonRanch 14 34.87608 -118.6028 1117.6300 3
## 809 2016 195 TejonRanch 15 34.87612 -118.6026 1117.1640 3
## 811 2016 196 TejonRanch 16 34.87618 -118.6027 1117.7140 3
## 813 2016 197 TejonRanch 17 34.87619 -118.6026 1117.7420 3
## 815 2016 198 TejonRanch 18 34.87619 -118.6026 1118.2090 3
## 817 2016 199 TejonRanch 19 34.87620 -118.6026 1118.0880 3
## 819 2016 200 TejonRanch 20 34.87606 -118.6026 1117.5560 3
## 821 2016 201 TejonRanch 21 34.87646 -118.6018 1117.7609 3
## 823 2016 202 TejonRanch 22 34.87641 -118.6017 1116.6505 3
## 825 2016 203 TejonRanch 23 34.87638 -118.6018 1117.1451 3
## 827 2016 204 TejonRanch 24 34.87639 -118.6018 1117.3970 3
## 829 2016 205 TejonRanch 25 34.87640 -118.6018 1117.1824 3
## 831 2016 206 TejonRanch 26 34.87633 -118.6019 1115.8668 3
## 833 2016 207 TejonRanch 27 34.87628 -118.6019 1116.7532 3
## 835 2016 208 TejonRanch 28 34.87625 -118.6020 1116.4640 3
## 837 2016 209 TejonRanch 29 34.87627 -118.6020 1116.5480 3
## 839 2016 210 TejonRanch 30 34.87623 -118.6020 1117.2290 3
## Abundance Richness Biomass
## 1 0.116161616 -0.50000000 0.7119778644
## 3 0.087818697 -0.60000000 0.4796531205
## 5 0.080906149 -0.33333333 0.6615644834
## 7 -0.121568627 -0.50000000 0.2712539822
## 9 0.049853372 -0.50000000 0.4593215503
## 11 0.184357542 -0.50000000 0.6795536250
## 13 0.261780105 -0.50000000 0.4608192877
## 15 -0.252427184 0.00000000 0.4919018585
## 17 -0.174887892 -0.33333333 0.6526389444
## 19 0.014423077 -0.50000000 0.5086644867
## 21 0.139534884 -0.33333333 0.3631851602
## 23 0.210653753 -0.50000000 0.5080463308
## 25 -0.780104712 0.00000000 0.4349842452
## 27 0.311827957 -0.50000000 0.7702038806
## 29 0.336708861 -0.60000000 0.2323219991
## 31 0.392670157 -0.50000000 0.3712568032
## 33 0.110526316 -0.33333333 0.2790786494
## 35 0.290322581 -0.33333333 0.8636516386
## 37 0.111111111 -0.33333333 0.6788779257
## 39 0.144508671 -0.33333333 0.7595415671
## 41 0.412300683 -0.50000000 0.8449457768
## 43 0.419354839 -0.50000000 -0.1049540415
## 45 0.357798165 -0.50000000 0.6227795285
## 47 0.219858156 -0.33333333 -0.0902199449
## 49 0.330000000 -0.33333333 0.6187907821
## 51 0.126760563 -0.33333333 0.7574940805
## 53 0.248908297 -0.50000000 0.5044125134
## 55 0.040767386 -0.50000000 0.5665006524
## 57 0.323456790 0.00000000 0.7189177620
## 59 0.307462687 0.00000000 0.4426137567
## 61 0.475504323 -0.66666667 0.6770310149
## 63 0.223529412 -0.33333333 0.8100986434
## 65 0.444743935 -0.20000000 0.6336056940
## 67 -0.023529412 -0.50000000 0.7814083673
## 69 0.191637631 -0.60000000 -0.0870758305
## 71 -0.360655738 -0.20000000 0.1184504218
## 73 0.238434164 0.00000000 0.8905726334
## 75 0.341772152 -0.60000000 0.9400286944
## 77 -0.789473684 -0.50000000 0.7695158398
## 79 0.235955056 -0.20000000 0.5964276417
## 81 0.318750000 -0.50000000 0.4039094315
## 83 -0.035087719 -0.20000000 0.4961624763
## 85 -0.100671141 -0.20000000 0.4602290313
## 87 -0.107142857 -0.20000000 0.4550573406
## 89 0.077844311 -0.50000000 0.5893232187
## 91 0.275362319 -0.20000000 0.7070642723
## 93 -0.166666667 0.00000000 0.4056391915
## 95 0.575757576 -0.20000000 0.8496008659
## 97 -0.105882353 0.00000000 0.4560603999
## 99 -0.185185185 -0.60000000 0.3895348185
## 101 0.166666667 -0.33333333 0.6450904620
## 103 0.012448133 -0.60000000 0.5448609160
## 105 0.026548673 -0.50000000 0.5547202132
## 107 -0.362637363 -0.33333333 0.2152793275
## 109 -0.083969466 -0.33333333 0.4733767791
## 111 -0.041420118 -0.50000000 0.5058660462
## 113 -0.178294574 -0.50000000 0.3955781995
## 115 -0.465648855 0.00000000 0.0938392832
## 117 0.292134831 -0.60000000 0.7160642623
## 119 0.300000000 -0.20000000 0.7202401148
## 121 -0.069767442 0.00000000 0.8483307317
## 123 0.670886076 0.50000000 0.8016949153
## 125 -0.258064516 0.50000000 0.8142623553
## 127 0.400000000 -0.20000000 0.7902743020
## 129 -0.312500000 0.50000000 0.4274566970
## 131 -0.052631579 -0.33333333 0.5074892105
## 133 -0.368421053 0.00000000 0.4073927026
## 135 -0.037037037 0.33333333 0.6696825136
## 137 0.222222222 0.20000000 0.4387597894
## 139 -0.179487179 0.00000000 0.2695463320
## 141 0.312500000 0.00000000 0.4541074440
## 143 -0.028571429 -0.50000000 0.1521536453
## 145 -0.777777778 0.00000000 0.5596072931
## 147 0.076923077 0.00000000 0.8679251279
## 149 -0.272727273 -0.20000000 0.7130066743
## 151 -0.150000000 0.00000000 0.5380087283
## 153 -0.125000000 -0.50000000 0.9397717963
## 155 -0.034482759 0.33333333 0.4481977343
## 157 -0.153846154 0.20000000 0.3940984309
## 159 -0.161290323 0.14285714 0.6146335434
## 161 -0.021276596 -0.33333333 0.2152743275
## 163 0.333333333 0.00000000 0.7832666333
## 165 -0.034482759 0.00000000 0.6498431394
## 167 0.142857143 0.20000000 0.8402163451
## 169 -0.476190476 -0.14285714 0.3567608862
## 171 -0.076923077 0.33333333 0.6519212141
## 173 -0.111111111 0.00000000 0.2074660006
## 175 0.000000000 0.50000000 0.8967097043
## 177 0.111111111 0.33333333 0.9217960271
## 179 -0.448275862 -0.50000000 -0.5522570506
## 181 0.411764706 -0.33333333 0.8533612299
## 183 1.000000000 1.00000000 1.0000000000
## 185 0.333333333 0.20000000 0.5700227101
## 187 0.222222222 0.00000000 0.1455301455
## 189 0.310344828 -0.25000000 0.2102272727
## 191 0.714285714 0.60000000 0.6671493587
## 193 0.172413793 -0.20000000 0.0506990062
## 195 0.391304348 -0.14285714 0.7011118378
## 197 -0.111111111 -0.14285714 0.2945083620
## 199 0.000000000 0.20000000 0.9061425061
## 201 -0.083333333 0.00000000 0.3469970429
## 203 0.285714286 0.00000000 0.7982496545
## 205 0.000000000 0.14285714 -0.1149301826
## 207 1.000000000 1.00000000 1.0000000000
## 209 0.304347826 0.00000000 0.7495439299
## 211 0.294117647 0.20000000 0.7542619910
## 213 0.272727273 0.33333333 0.8833221251
## 215 0.176470588 0.60000000 0.8143354903
## 217 0.214285714 -0.14285714 0.8192456119
## 219 -0.142857143 0.14285714 0.3285299495
## 221 0.354838710 0.50000000 -0.2584942085
## 223 0.081081081 0.00000000 0.0354693197
## 225 0.058823529 -0.33333333 0.5296251511
## 227 1.000000000 1.00000000 1.0000000000
## 229 0.083333333 -0.20000000 0.8066840601
## 231 1.000000000 1.00000000 1.0000000000
## 233 1.000000000 1.00000000 1.0000000000
## 235 1.000000000 1.00000000 1.0000000000
## 237 0.250000000 0.33333333 0.6100965831
## 239 1.000000000 1.00000000 1.0000000000
## 241 0.461538462 0.20000000 0.4317400039
## 243 0.120000000 0.00000000 -0.0587628553
## 245 -0.360000000 0.00000000 0.6923697110
## 247 0.750000000 0.42857143 0.6393058945
## 249 0.777777778 0.60000000 0.7798188223
## 251 -0.120000000 0.00000000 0.4696642911
## 253 1.000000000 1.00000000 1.0000000000
## 255 -0.272727273 -0.20000000 1.0000000000
## 257 1.000000000 1.00000000 0.9201925608
## 259 -0.333333333 0.50000000 0.5734497079
## 261 0.000000000 0.00000000 1.0000000000
## 263 1.000000000 1.00000000 1.0000000000
## 265 0.391304348 0.11111111 0.7209858818
## 267 0.285714286 -0.14285714 -0.3734034560
## 269 0.166666667 0.14285714 0.7327057183
## 271 0.120000000 0.14285714 0.3756266122
## 273 0.000000000 0.00000000 0.0000000000
## 275 1.000000000 1.00000000 1.0000000000
## 277 0.818181818 0.33333333 0.4608250720
## 279 0.466666667 -0.14285714 0.6183624064
## 281 0.428571429 0.00000000 0.7515356072
## 283 0.176470588 -0.20000000 0.8261728100
## 285 0.428571429 0.20000000 0.9261516655
## 287 0.428571429 0.14285714 0.9148376772
## 289 0.333333333 0.00000000 0.1529029029
## 291 0.272727273 0.33333333 0.4494782901
## 293 0.555555556 0.20000000 0.3213861292
## 295 0.833333333 0.50000000 0.7620473727
## 297 0.529411765 0.33333333 0.7677135242
## 299 -0.466666667 -0.20000000 -0.0003025352
## 301 0.541666667 0.00000000 0.8890477983
## 303 0.285714286 0.33333333 0.5099159664
## 305 1.000000000 1.00000000 -0.3159687437
## 307 0.478260870 0.00000000 -0.0193116226
## 309 0.548387097 -0.33333333 -0.7561455261
## 311 0.488372093 0.33333333 0.6375491617
## 313 -0.170731707 -0.33333333 0.7590221187
## 315 0.297297297 -0.33333333 0.8505929820
## 317 0.085714286 0.00000000 0.5696080789
## 319 0.265306122 0.00000000 0.9412910878
## 321 0.416666667 0.00000000 0.6330641418
## 323 0.351351351 0.00000000 0.9117237890
## 325 0.032258065 -0.33333333 0.6542429594
## 327 -0.176470588 -0.33333333 0.9460873064
## 329 -0.333333333 0.20000000 0.4748446582
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## 603 -0.714285714 -0.50000000 -0.6126435068
## 605 0.000000000 0.00000000 -0.5626066874
## 607 0.636363636 0.33333333 0.0541506322
## 609 0.444444444 0.00000000 0.3603976342
## 611 0.428571429 0.20000000 0.1440466750
## 613 0.100000000 0.11111111 -0.1163394391
## 615 0.666666667 0.60000000 0.3524324736
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## 625 -0.500000000 0.00000000 -0.3983889528
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## 647 0.000000000 0.20000000 0.0419393939
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## 685 0.000000000 0.00000000 0.1361361361
## 687 1.000000000 1.00000000 1.0000000000
## 689 0.000000000 0.00000000 0.0000000000
## 691 0.333333333 0.33333333 0.0359679267
## 693 1.000000000 1.00000000 1.0000000000
## 695 0.000000000 0.00000000 1.0000000000
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## 699 -1.000000000 -1.00000000 -1.0000000000
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## 757 -0.125000000 -0.60000000 0.2539237668
## 759 0.346938776 -0.66666667 -0.1012083626
## 761 0.289473684 0.00000000 0.1958249759
## 763 0.257142857 -0.20000000 0.0970992480
## 765 0.192982456 -0.33333333 0.7428781261
## 767 -0.236363636 0.00000000 0.8792069086
## 769 -0.128205128 -0.20000000 0.1232449298
## 771 0.407407407 0.00000000 0.6149531455
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## 775 0.317073171 -0.20000000 0.2104357140
## 777 -0.120000000 0.20000000 -0.1236287160
## 779 -0.160000000 0.33333333 0.4350836408
## 781 0.382716049 0.20000000 -0.0125921551
## 783 0.303030303 0.00000000 -0.0013919268
## 785 -0.062500000 0.33333333 0.1931782458
## 787 -0.333333333 0.14285714 -0.8667356779
## 789 0.138461538 0.25000000 0.4275430035
## 791 0.000000000 0.33333333 0.6825802976
## 793 0.163636364 0.14285714 0.5992973553
## 795 -0.222222222 -0.20000000 -0.0999432785
## 797 0.000000000 -0.11111111 -0.6727514596
## 799 0.161290323 -0.11111111 0.6025874346
## 801 0.196581197 -0.14285714 -0.4921950789
## 803 0.015384615 0.14285714 -0.0394353574
## 805 -0.475409836 0.20000000 -0.4762100212
## 807 0.170731707 0.25000000 -0.3190357440
## 809 0.052631579 0.00000000 0.7788296402
## 811 0.093525180 -0.09090909 -0.5659009699
## 813 0.156626506 0.25000000 -0.2578781513
## 815 0.160493827 -0.14285714 0.0833863781
## 817 -0.217391304 -0.42857143 0.5182010956
## 819 0.373134328 0.33333333 0.2026123137
## 821 0.250000000 -0.42857143 -0.1397175313
## 823 -0.345454545 -0.20000000 0.3225661732
## 825 -0.153846154 -0.14285714 -0.5790226460
## 827 0.243902439 0.33333333 -0.1462200957
## 829 -0.075000000 -0.50000000 0.5087807832
## 831 -0.254901961 0.11111111 0.3546859422
## 833 0.225806452 0.00000000 0.6949414737
## 835 0.026315789 0.33333333 0.0135630499
## 837 0.111111111 0.14285714 -0.0244374546
## 839 -0.016949153 0.00000000 0.2277740481rii.mean <- rii.dat %>% group_by(Year, Gradient, Site) %>% summarize(avg=mean(Biomass),se=se(Biomass))
rii.mean <- data.frame(rii.mean)
plot(rii.mean[rii.mean$Year==2016,"Gradient"]-.1,rii.mean[rii.mean$Year==2016,"avg"], ylim=c(-1,1), pch=19, cex=1.5, xlim=c(0.5,7.5), ylab="Rii Biomass", xlab="Site", xaxt="n", cex.lab=1.5)
axis(1, 1:7, lab=unique(rii.mean$Site))
error.bar(rii.mean[rii.mean$Year==2016,"Gradient"]-.1,rii.mean[rii.mean$Year==2016,"avg"],rii.mean[rii.mean$Year==2016,"se"])
error.bar(rii.mean[rii.mean$Year==2017,"Gradient"]+.1,rii.mean[rii.mean$Year==2017,"avg"],rii.mean[rii.mean$Year==2017,"se"])
points(rii.mean[rii.mean$Year==2017,"Gradient"]+.1,rii.mean[rii.mean$Year==2017,"avg"], pch=21, bg="Grey50", cex=1.5)
abline(h=0, lwd=2, lty=2)
legend(6.6,-0.65, c("2016","2017"), pch=22, pt.bg=c("Black","Grey50"), cex=1.6)
## add year column to precipitation data
precip[,"Year"] <- ifelse(precip$season=="season.1","2016","2017")
rii.precip <- merge(precip,rii.mean, by=c("Year","Gradient"))
ihs <- function(x) {
y <- log(x + sqrt(x ^ 2 + 1))
return(y)
}
## Precipitation vs Rii
plot(log(rii.precip[,"Precip"]), rii.precip[,"avg"], ylab= "RII", xlab="log precipitation Precipitation", pch=19, cex=1.5, cex.lab=1.5, cex.axis=1.3)
abline(h=0, lwd=2, lty=2)
m1 <- lm(rii.precip[,"avg"] ~ rii.precip[,"Precip"])